• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.434-437
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• 1989

This paper describes the speed control of d.c. servo motor by PID method in loads. PID algorithm has mainly been used in industrial circles In spite of the development of various control theory. D.C. motor speed is controlled by a microprocessor (Z-80). The speed control of d.c. motor is experimented in transient and steady state. In this study, feedforward controller Is used for dealing with loads. When it is possible to measure loads, this feed forward controller is used with another controller. And also, satisfying control effect Is gotten by using it In system with loads. Therefore, It is proved through experiment that a new designed controller can control the speed of d.c. servo motor.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1180-1182
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• 1999

Modeling for utility interactive photovoltaic power generation system has been studied using PSCAD/EMTDC. The proposed model system consists of a simple utility circuit configuration, 3kW of single phase utility interactive photovoltaic system, single phase PWM voltage source inverter module, and feed forward PID controller as control circuit. In the system, the DC current is assumed constant, and the voltage source inverter provides sinusoidal ac current for the loads of utility system. The simulation results are given in order to verify the effectiveness of the proposed model. The phases of output voltage of utility system and the output current of the inverter module are compared. Especially, the compensation effect of the photovoltaic system for the unbalanced load is analyzed. and the transient phenomena for a phase to ground fault are also simulated.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.516-518
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• 1999

This paper presents an feed-forward neural network design instead PI controller for the speed control of an Induction Motor. The design employs the training strategy with Neural Network Controller(NNC) and Neural Network Emulator(NNE). Emulator identifies the motor by simulating the input and output map. In order to update the weights of the Controller. Emulator supplies the error path to the output stage of the controller using backpropagation algorithm. and then Controller produces an adequate output to the system due to neural networks learning capability. Therefore it becomes adjustable to the system with changing characteristics caused by a load. The speed control based on neural networks for induction motor is implemented by a vector controlled induction motor. The simulation results demonstrate that actual motor speed with neural network system well follows the reference speed minimizing the error and is available to implement on the vector control theory.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.5
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• pp.828-834
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• 1997

The knowledge of material stress-strain behavior is an essential requirement for design and analysis of deformation processes. Empirical stress-strain relationship and constitutive equations describing material behavior during deformation are being widely used, despite suffering some drawbacks in terms of ease of development, accuracy and speed. In the present study, back-propagation neural networks are used to model and predict the flow stresses of a HSLA steel under conditions of constant strain, strain rate and temperature. The performance of the network model is comparedto those of statistical models on rate equations. Well-trained network model provides fast and accurate results, making it superior to statistical models.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.23 no.7 s.166
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• pp.1196-1204
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• 1999

adaption of the model predictions is highly desirable. In general, the width deviation at the head and tail ends of strip may be different from that of the steady state region. Therefore, the dynamic edger corrections can be used to compensate the width deviations which would otherwise occur. For the precise width control, the effect of edger roll gap and rolling conditions on the width deviation of head and tail ends of strip has been investigated and the effective method to decrease width deviation has been proposed. On-line application of dynamic edger control method in this study shows about 50% width compensation at the head end of the strip, and near perfect compensation at the tail end of strip.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.23 no.11 s.170
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• pp.1929-1939
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• 1999

This paper presents a neural network approach, which was named PRONET, to 3D object recognition and pose calculation. 3D objects are represented using a set of centroidal profile patterns that describe the boundary of the 2D views taken from evenly distributed view points. PRONET consists of the training stage and the execution stage. In the training stage, a three-layer feed-forward neural network is trained with the centroidal profile patterns using an error back-propagation method. In the execution stage, by matching a centroidal profile pattern of the given image with the best fitting centroidal profile pattern using the neural network, the identity and approximate orientation of the real object, such as a workpiece in arbitrary pose, are obtained. In the matching procedure, line-to-line correspondence between image features and 3D CAD features are also obtained. An iterative model posing method then calculates the more exact pose of the object based on initial orientation and correspondence.

• Journal of the Korean Society of Propulsion Engineers
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• v.15 no.2
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• pp.1-7
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• 2011

The UAV(Unmanned Aerial Vehicle) which is remotely operating with long endurance in high altitude must have a very reliable propulsion system. The precise fault diagnostic system of the turboprop engine as a propulsion system of this type UAV can promote reliability and availability. This work proposes a diagnostic method which can identify the faulted components from engine measuring parameter changes using Fuzzy Logic and quantify its faults from the identified fault pattern using Neural Network Algorithms. It is found by evaluation examples that the proposed diagnostic method can detect well not only single type faults but also multiple type faults.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.23 no.5
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• pp.376-382
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• 2004

The interaction between a vibrating structure and a surrounding acoustic medium determines the acoustic power propagating into the far-field. A straightforward method to reduce the radiated power is to reduce the vibration of the structure. However it is more efficient to control the modes of the structure separately since each vibration mode of the structure has different radiation efficiency. An efficient method to reduce the sound radiation in the low frequency region is proposed by reducing the radiation efficiency of the structure. Numerical simulations are carried out for a simply-supported beam in which the feed-forward control is applied to reduce the volume velocity of each structural mode. This method is found to be very efficient in reducing low frequency sound radiation.