• Proceedings of the KIEE Conference
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• 1999.07f
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• pp.2610-2612
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• 1999

In controlling vector of induction motor, PID controller is required much time as the expert should control manually a gain of controller according to plant or a change of circumstances. Accordingly, this paper has gotten a gain of PID controller used neural network by self-funning method in order to settle above problem. The neural network can describe an input/output features in spite of non-linear system which is hard to get mathematical model by controlling the strength of connection by learning. It has a strong character against a distortion and noise of input information, and is suitable modeling of diver-variable system which is composed of several input/output. This paper has represented the self-tunning method for gain of PID controller used neural network when using PID controller to control speed of induction motor, and has checked strong characters against distortion and noise of input information through simulation.

• International Journal of Concrete Structures and Materials
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• v.19 no.1E
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• pp.11-16
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• 2007

Recently, probabilistic neural network (PNN) has been proposed to predict the compressive strength of concrete for the known effect of improvement on PNN by the iteration method. However, an empirical method has been incorporated in the PNN technique to specify its smoothing parameter, which causes significant uncertainty in predicting the compressive strength of concrete. In this study, a modified probabilistic neural network (MPNN) approach is hence proposed. The global probability density function (PDF) of variables is reflected by summing the heterogeneous local PDFs which are automatically determined by the individual standard deviation of each variable. The proposed MPNN is applied to predict the compressive strength of concrete using actual test data from a concrete company. The estimated results of MPNN are compared with those of the conventional PNN. MPNN showed better results than the conventional PNN in predicting the compressive strength of concrete and provided promising results for the probabilistic approach to predict the concrete strength by using the individual standard deviation of a variable.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.1
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• pp.43-48
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• 2002

This paper discusses the application of fuzzy-ARTMAP neural network to compensate the nonlinearity of satellite communication channel. The fuzzy-ARTMAP is the class of ART(adaptive resonance theory) architectures designed fur supervised loaming. It has capabilities not fecund in other neural network approaches, that includes a small number of parameters, no requirements fur the choice of initial weights, automatic increase of hidden units, and capability of adding new data without retraining previously trained data. By a match tracking process with vigilance parameter, fuzzy-ARTMAP neural network achieves a minimax teaming rule that minimizes predictive error and maximizes generalization. Thus, the system automatically leans a minimal number of recognition categories, or hidden units, to meet accuracy criteria. As a input-converting process for implementing fuzzy-ARTMAP equalizer, the sigmoid function is chosen to convert actual channel output to the proper input values of fuzzy-ARTMAP. Simulation studies are performed over satellite nonlinear channels. QPSK signals with Gaussian noise are generated at random from Volterra model. The performance of proposed fuzzy-ARTMAP equalizer is compared with MLP equalizer.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.705-712
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• 2004

In this paper, detection of rotor position at standstill of toroidal switched reluctance motor(TSRM) with built-in search coils using neural network is proposed. When search coils are used as a position sensor, it has many advantages like low cost, decrease in the volume, high robust characteristics and wide applications. However, the initial rotor position detection is very difficult because the search coil's EMF doesn't exist at standstill. In this paper, detection of initial rotor position of TSRM with built-in search coils using neural network is suggested. The experiment for the proposed method are presented. As a result of that, the accuracy and validity of the proposed method is verified.

• Journal of The Korean Astronomical Society
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• v.47 no.6
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• pp.209-214
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• 2014

In this study we develop a set of solar proton event (SPE) forecast models with NOAA scales by Multi Layer Perceptron (MLP), one of neural network methods, using GOES solar X-ray flare data from 1976 to 2011. Our MLP models are the first attempt to forecast the SPE scales by the neural network method. The combinations of X-ray flare class, impulsive time, and location are used for input data. For this study we make a number of trials by changing the number of layers and nodes as well as combinations of the input data. To find the best model, we use the summation of F-scores weighted by SPE scales, where F-score is the harmonic mean of PODy (recall) and precision (positive predictive value), in order to minimize both misses and false alarms. We find that the MLP models are much better than the multiple linear regression model and one layer MLP model gives the best result.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.97-100
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• 1997

In order to predict the moldability of a injection molded part, a simulation of filling is needed. Especially when short shot is predicted by CAE simulation in the filling stage, there are mainly three ways to solve the problem. Modification of gate and runner, replacement of plastic resin, and adjustment of process conditions are the main ways. Among them, adjustment of process conditions is the most economic way in the cost and time since the mold doesn\\`t need t be modified at all. But it is difficult to adjust the process conditions appropriately in no times since it requires an empirical knowledge of injection molding. In this paper, a fuzzy neural network(FNN) based upon injection molding process is proposed to evaluate moldability in filling stage and also to solve the problem in case of short shot. An adequate mold temperature is generated through the fuzzy neural network where fill time and melt temperature are taken into considerations because process conditions affect each other.

• Structural Monitoring and Maintenance
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• v.3 no.3
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• pp.297-314
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• 2016

As the study of internal corrosion of pipeline need a large number of experiments as well as long time, so there is a need for new computational technique to expand the spectrum of the results and to save time. The present work represents a new non-destructive evaluation (NDE) technique for detecting the internal corrosion inside pipeline by evaluating the dielectric properties of steel pipe at room temperature by using electrical capacitance sensor (ECS), then predict the effect of pipeline environment temperature (${\theta}$) on the corrosion rates by designing an efficient artificial neural network (ANN) architecture. ECS consists of number of electrodes mounted on the outer surface of pipeline, the sensor shape, electrode configuration, and the number of electrodes that comprise three key elements of two dimensional capacitance sensors are illustrated. The variation in the dielectric signatures was employed to design electrical capacitance sensor (ECS) with high sensitivity to detect such defects. The rules of 24-electrode sensor parameters such as capacitance, capacitance change, and change rate of capacitance are discussed by ANSYS and MATLAB, which are combined to simulate sensor characteristic. A feed-forward neural network (FFNN) structure are applied, trained and tested to predict the finite element (FE) results of corrosion rates under room temperature, and then used the trained FFNN to predict corrosion rates at different temperature using MATLAB neural network toolbox. The FE results are in excellent agreement with an FFNN results, thus validating the accuracy and reliability of the proposed technique and leads to better understanding of the corrosion mechanism under different pipeline environmental temperature.

• Computers and Concrete
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• v.21 no.1
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• pp.47-54
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• 2018

Artificial neural network models can be successfully used to simulate the complex behavior of many problems in civil engineering. As compared to conventional computational methods, this popular modeling technique is powerful when the relationship between system parameters is intrinsically nonlinear, or cannot be explicitly identified, as in the case of concrete behavior. In this investigation, an artificial neural network model was developed to assess the residual compressive strength of self-compacted concrete at elevated temperatures ($20-900^{\circ}C$) and various relative humidity conditions (28-99%). A total of 332 experimental datasets, collected from available literature, were used for model calibration and verification. Data used in model development incorporated concrete ingredients, filler and fiber types, and environmental conditions. Based on the feed-forward back propagation algorithm, systematic analyses were performed to improve the accuracy of prediction and determine the most appropriate network topology. Training, testing, and validation results indicated that residual compressive strength of self-compacted concrete, exposed to high temperatures and relative humidity levels, could be estimated precisely with the suggested model. As illustrated by statistical indices, the reliability between experimental and predicted results was excellent. With new ingredients and different environmental conditions, the proposed model is an efficient approach to estimate the residual compressive strength of self-compacted concrete as a substitute for sophisticated laboratory procedures.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.4
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• pp.21-28
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• 2004

This paper presents a direct multivariable self-tuning controller using neural network which adapts to the changing parameters of the higher order multivariable nonlinear system with nonminimum phase behavior, mutual interactions and time delays. The nonlinearities are assumed to be globally bounded, and a multivariable nonlinear system is divided linear part and nonlinear part. The neural network is used to estimate the controller parameters, and the control output is obtained through estimated controller parameter. In order to demonstrate the effectiveness of the proposed algorithm the computer simulation is done to adapt the multivariable nonlinear nonminimm phase system with time delays and changed system parameter after a constant time. The proposed method compared with direct multivariable adaptive controller using neural network.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.04a
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• pp.327-334
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• 2003

Thermal Prestressing Method for continuous composite girder bridges is a new design and construction method developed to induce initial composite stresses in the concrete slab at negative bending regions. Due to the induced initial stresses, prevention of tensile cracks at concrete slab, reduction of steel girder section, and reduction of reinforcing bars are possible. Thus, economical and construction efficiency can be improved. Method for determining optimum heating region of Thermal Prestressing Method, has not been established although such method is essential for increasing efficiency of the designing process. Trial-and-error method used in previous studies is far from efficient and more rational method for computing optimal heating region is required. In this study, efficient method for determining optimum heating region in the use of Thermal Prestressing Method is developed based on artificial neural network algorithm, which is widely adopted to pattern recognition, optimization, diagnosis, and estimation problems in various fields. Back-propagation algorithm, which is commonly used as a learning algorithm in neural network problems, is used for training of the neural network. Through case studies of 2-span continuous and 3-span continuous composite girder bridges using the developed process, the optimal heating regions are obtained.