• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.683-684
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• 2013

• Computers and Concrete
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• v.24 no.6
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• pp.555-560
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• 2019

During the application of shotcrete, a part of the concrete bounces back after hitting to the surface, the reinforcement or previously sprayed concrete. This rebound material is definitely not added to the mixture and considered as waste. In this study, a deep neural network model was developed to predict the rebound material during shotcrete application. The factors affecting rebound and the datasets of these parameters were obtained from previous experiments. The Long Short-Term Memory (LSTM) architecture of the proposed deep neural network model was used in accordance with this data set. In the development of the proposed four-tier prediction model, the dataset was divided into 90% training and 10% test. The deep neural network was modeled with 11 dependents 1 independent data by determining the most appropriate hyper parameter values for prediction. Accuracy and error performance in success performance of LSTM model were evaluated over MSE and RMSE. A success of 93.2% was achieved at the end of training of the model and a success of 85.6% in the test. There was a difference of 7.6% between training and test. In the following stage, it is aimed to increase the success rate of the model by increasing the number of data in the data set with synthetic and experimental data. In addition, it is thought that prediction of the amount of rebound during dry-mix shotcrete application will provide economic gain as well as contributing to environmental protection.

• International Journal of Concrete Structures and Materials
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• v.6 no.3
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• pp.177-186
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• 2012

The temperature distributions of concrete structures strongly depend on the value of thermal conductivity of concrete. However, the thermal conductivity of concrete varies according to the composition of the constituents and the temperature and moisture conditions of concrete, which cause difficulty in accurately predicting the thermal conductivity value in concrete. For this reason, in this study, back-propagation neural network models on the basis of experimental values carried out by previous researchers have been utilized to effectively account for the influence of these variables. The neural networks were trained by 124 data sets with eleven parameters: nine concrete composition parameters (the ratio of water-cement, the percentage of fine and coarse aggregate, and the unit weight of water, cement, fine aggregate, coarse aggregate, fly ash and silica fume) and two concrete state parameters (the temperature and water content of concrete). Finally, the trained neural network models were evaluated by applying to other 28 measured values not included in the training of the neural networks. The result indicated that the proposed method using a back-propagation neural algorithm was effective at predicting the thermal conductivity of concrete.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.1 no.1
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• pp.19-31
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• 2007

RFID tag is detected by an RFID antenna and information is read from the tag detected, by an RFID reader. RFID tag detection by an RFID reader is very important at the deployment stage. Tag detection is influenced by factors such as tag direction on a target object, speed of a conveyer moving the object, and the contents of an object. The water content of the object absorbs radio waves at high frequencies, typically approximately 900 MHz, resulting in unstable tag signal power. Currently, finding the best conditions for factors influencing the tag detection requires very time consuming work at deployment. Thus, a quick and simple RFID tag detection scheme is needed to improve the current time consuming trial-and-error experimental method. This paper proposes a back-propagation learning-based RFID tag detection prediction scheme, which is intelligent and has the advantages of ease of use and time/cost savings. The results of simulation with the proposed scheme demonstrate a high prediction accuracy for tag detection on a water content, which is comparable with the current method in terms of time/cost savings.

• Structural Engineering and Mechanics
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• v.38 no.4
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• pp.443-457
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• 2011

The loss of strength in a structure as a result of cyclic loads over a period of life time is an important phenomenon for the life-cycle analysis. Service loads are accentuated at the areas of stress concentration, mainly at the connection of components. Structural components unavoidably are affected by defects such as surface scratches, surface roughness and weld defects of random sizes, which usually occur during the manufacturing and handling process. These defects are shown to have an important effect on the fatigue life of the structural components by promoting crack initiation sites. The value of equivalent initial flaw size (EIFS) is calculated by using the back extrapolation technique and the Paris law of fatigue crack growth from results of fatigue tests. We try to analyze the effect of EIFS distribution in a multiple site damage (MSD) specimen by using the extended finite element method (XFEM). For the analysis, fatigue tests were conducted on the centrally-cracked specimens and MSD specimens.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.10a
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• pp.513-520
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• 2002

The stability analysis of rock slope can be predicted using a suitable field data but it cannot be predicted unless suitable field data was taken. In this study, artificial neural networks theory is applied to predict plane failure that has a few data. It is well known that human brain has the advantage of handling disperse and parallel distributed data efficiently. On the basis of this fact, artificial neural networks theory was developed and has been applied to various fields of science successfully In this study, error back-propagation algorithm that is one of the teaching techniques of artificial neural networks is applied to predict plane failure. In order to verify the applicability of this model, a total of 30 field data results are used. These data are used for training the artificial neural network model and compared between the predicted and the measured. The simulation results show the potentiality of utilizing the neural networks for effective safety factor prediction of plane failure. In conclusion, the well-trained artificial neural network model could be applied to predict the plane failure stability of rock slope.

• Journal of Power Electronics
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• v.15 no.1
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• pp.177-189
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• 2015

The output of the controller is said to exceed the input limits of the plant being controlled when a control system operates in a non-linear region. This process is called the windup phenomenon. The windup phenomenon is not preferable in the control system because it leads to performance degradation, such as overshoot and system instability. Many anti-windup strategies involve switching, where the integral component differently operates between the linear and the non-linear states. The range of state for the non-overshoot performance is better illustrated by the boundary integral error plane than the proportional-integral (PI) plane in windup inspection. This study proposes a PI controller with a separate closed-loop integral controller and reference value set with respect to the input command and external torque. The PI controller is compared with existing conventional proportional integral, conditional integration, tracking back calculation, and integral state prediction schemes by using ScicosLab simulations. The controller is also experimentally verified on a direct current motor under no-load and loading conditions. The proposed controller shows a promising potential with its ability to eliminate overshoot with short settling time using the decoupling mode in both conditions.

• Transactions of Materials Processing
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• v.21 no.7
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• pp.426-431
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• 2012

The work roll of a Sendzimir mill has a small diameter in comparison to its length, so it is easily deformed by the rolling pressure. It also has a complex back up roll system, so it is difficult to analyze the roll deformation. For this reason in Part I we have developed a model which predicts the radial displacement of the roll. In this paper, we apply the model to a Sendzimir mill and propose a new model for the prediction of the deformed roll profile in a Sendzimir mill. The prediction accuracy of the new model is demonstrated through comparison of the predictions from the FE model.

• Transactions of the Korean Society of Automotive Engineers
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• v.9 no.5
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• pp.148-156
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• 2001

Computational Fluid Dynamics (CFD) analysis was carried out to investigate exhaust gas flow and acoustic characteristics in the exhaust system of a passenger car. Transient 3-dimensional flow field in the front and rear mufflers was simulated by CFD and far-field sound pressure was modeled by a simple monopole source method. Engine performance simulation was also performed to obtain the boundary condition of instantaneous fluid flow variation at the inlet of the exhaust system. Detailed exhaust gas flow characteristics such as velocity and pressure distribution inside the mufflers were presented and the pulsating pressure amplitude was compared at several positions in the exhaust system to deduce sound pressure level. The present method of the acoustic analysis coupled with CFD techniques would be very effective for the prediction of sound noise from vehicle exhaust systems although the effects of the inlet boundary condition and heat transfer on the accuracy of the prediction have to be validated through further studies.

• The Korean Journal of Applied Statistics
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• v.22 no.5
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• pp.971-979
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• 2009

In this paper, we introduce the method that reduces the bias when the transformation and back-transformation approach is applied in GARCH models. A parametric bootstrap is employed to compute the conditional expectation which is the prediction value to minimize mean square errors in the original scale. Through the analyese of returns of KOSPI and KOSDAQ, we verified that the proposed method provides a bias-reduced estimation for the prediction value.