• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.329-331
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• 2006

Load models are important for improving the accuracy of stability analysis. Load characteristics are changed for voltage and frequency condition. In this research, ANN with LMBP learning rule is used to construct the load model. Characteristics of some residential loads are tested under various voltage and frequency conditions. Acquired data are used to construct load models by ANN. Constructed ANN load model are applied to transient stability analysis.

• Structural Engineering and Mechanics
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• v.52 no.1
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• pp.97-113
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• 2014

In the present work appropriate concrete material models have been proposed to predict drying shrinkage and specific creep of High-performance concrete (HPC) using Artificial Neural Network (ANN). The ANN models are trained, tested and validated using 106 different experimental measured set of data collected from different literatures. The developed models consist of 12 input parameters which include quantities of ingredients namely ordinary Portland cement, fly ash, silica fume, ground granulated blast-furnace slag, water, and other aggregate to cement ratio, volume to surface area ratio, compressive strength at age of loading, relative humidity, age of drying commencement and age of concrete. The Feed-forward backpropagation networks with Levenberg-Marquardt training function are chosen for proposed ANN models and same implemented on MATLAB platform. The results shows that the proposed ANN models are more rational as well as computationally more efficient to predict time-dependent properties of drying shrinkage and specific creep of HPC with high level accuracy.

• Korean Journal of Construction Engineering and Management
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• v.6 no.5 s.27
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• pp.69-77
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• 2005

Change is inevitable and is a reality of construction projects. Most construction contracts include change clauses and allowing contractors an equitable adjustment to the contract price and duration caused by change. However, the actions of a contractor can cause a loss of productivity and furthermore can result in disruption of the whole project because of a cumulative or ripple effect. Because of its complicated nature, it becomes a complex issue to determine the cumulative impact (ripple effect) caused by single or multiple change orders. Furthermore, owners and contractors do not always agree on the adjusted contract price for the cumulative Impact of the changes. A number of studies have attempted to quantify the impact of change orders on project costs and schedule. Many of these attempted to develop regression models to quantify the loss. However, regression analysis has shortcomings in dealing with many qualitative or noisy input data. This study develops ANN models to classify and quantify the labor productivity losses that are caused by the cumulative impact of change orders. The results skew that ANN models give significantly improved performance compared to traditional statistical models.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.563-565
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• 2005

This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor(SynRM) which minimizes the copper and iron losses. ALso, this paper presents a sensorless control scheme of SynRM using artificial neural network(ANN). The proposed algorithm allows the electromagnetic losses in variable speed and torque drives to be reduced while keeping good torque control dynamics. The control performance of ANN is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm

• ETRI Journal
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• v.31 no.1
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• pp.92-94
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• 2009

We propose a system for mobile-phone fraud detection based on a bidirectional artificial neural network (bi-ANN). The key advantage of such a system is the ability to detect fraud not only by offline processing of call detail records (CDR), but also in real time. The core of the system is a bi-ANN that predicts the behavior of individual mobile-phone users. We determined that the bi-ANN is capable of predicting complex time series (Call_Duration parameter) that are stored in the CDR.

• Journal of the Korean Society for Precision Engineering
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• v.34 no.1
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• pp.23-27
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• 2017

This paper aims to develop a prediction model for the product quality of a casting process. Prediction of the product quality utilizes an artificial neural network (ANN) in order to renovate the manufacturing technology of the root industry. Various aspects of the research on the prediction algorithm for the casting process using an ANN have been investigated. First, the key process parameters have been selected by means of a statistics analysis of the process data. Then, the optimal number of the layers and neurons in the ANN structure is established. Next, feed-forward back propagation and the Levenberg-Marquardt algorithm are selected to be used for training. Simulation of the predicted product quality shows that the prediction is accurate. Finally, the proposed method shows that use of the ANN can be an effective tool for predicting the results of the casting process.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1997.03a
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• pp.144-147
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• 1997

Process design of multi-step wire drawing process, conducted by means of finite element analysis and ANN(Artificial Neural Network), has been considered. The investigated problem involves the adequate selection of the drawing die angle and the correspondent reduction rate sequence in the condition of desired initial and final diameter. Combinations of the process parameters which are used in finite element simulation are selected by using orthogonal array. Also the orthogonal array and the results of finite element simulation which are related to the process energy are used as train data of ANN. In this study, it is shown that the new technique using ANN is useful method in application to the wide range of metal forming process.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2004.09a
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• pp.307-311
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• 2004

The unsaturated zone is a significant pathway of the surface contaminant movement and is a highly heterogeneous medium. Therefore, there are limitations in applying conventional convection-dispersion equation(CDE). Artificial neural network(ANN) is considered to be a versatile tool for approximating complex functions. For evaluating the applicability of ANN, numerical tests using ANN were conducted with training set generated by HYDRUS-2D which is based on CDE. The results represent that ANN can estimate the solute transport and the choice of network parameters and generation of training set patterns are important for efficient estimation.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.216-218
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• 2006

The paper is proposed high performance control of induction motor drive with adaptive fuzzy logic controller(AFLC). Also, this paper is proposed speed control of induction motor using AFLC and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The proposed control algorithm is applied to induction motor drive system controlled AFLC and ANN controller. And this paper is proposed the results to verify the effectiveness of the AFLC and ANN controller.

• Structural Engineering and Mechanics
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• v.65 no.4
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• pp.447-452
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• 2018

The controlling and prediction of spring back is one of the most important factors in sheet metal forming processes which require high dimensional precision. The relationship between effective parameters and spring back phenomenon is highly nonlinear and complicated. Moreover, the objective function is implicit with regard to the design variables. In this paper, first the influence of some effective factors on spring back in U-die bending process was studied through some experiments and then regarding the robustness of artificial neural network (ANN) approach in predicting objectives in mentioned kind of problems, ANN was used to estimate a prediction model of spring back. Eventually, the spring back angle was optimized using the Imperialist Competitive Algorithm (ICA). The results showed that the employment of ANN provides us with less complicated and time-consuming analytical calculations as well as good results with reasonable accuracy.