• International Journal of Concrete Structures and Materials
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• v.3 no.1
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• pp.71-77
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• 2009

A theoretical model of multiple cracking failure mechanism is proposed herein for fiber reinforced high performance Cementitious composites. By introducing partial debonding energy dissipation on non-first cracking plane and fiber reinforcing parameter, the failure mechanism model of multiple cracking is established based on the equilibrium assumption of total energy dissipation on the first crack plane and non-first cracking plane. Based on the assumption of the first crack to be the final failure crack, energy dissipation terms including complete debonding energy, partial debonding energy, strain energy of steel fiber, frictional energy, and matrix fracture energy have been modified and simplified. By comparing multiple cracking number and energy dissipations with experiment results of the reference's data, it indicates that this model can describe the multiple cracking behavior of fiber reinforced high performance cementitious composites and the influence of the partial debonding term on energy dissipation is significant. The model proposed may lay a foundation for the predictions of the first cracking capacity and post cracking capacity of fiber reinforced high performance cementitious composites and also can be a reference for optimal mixture for construction cost.

• The Journal of the Acoustical Society of Korea
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• v.22 no.1E
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• pp.33-42
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• 2003

Kuo et al proposed an on-line method for an adaptive prediction error filter for improving secondary path modeling performance in the modeling method of the secondary path. This method have some disadvantages, namely having to use additive noise with the result that noise control performance is not good since it is focused on the estimated performance of the secondary path. In this paper, we proposes a modified Kuo model using gain control parameter and delay. It uses a reference signal for additive noise to improve the problems in the existing Kuo model.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.11
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• pp.531-535
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• 2000

A method to compute the reactive powers of the added buses by the decoupled UPFC model for the optimal voltage profile is presented, by which the voltage magnitudes of PQ buses can get closer to the reference value(usually one p.u.). The performance index for assessing how much the voltage magnitude is closer to the reference value is defined as the squared sum of the present voltage minus the reference voltage multiplied by the weighting number associated with the relative importance of the buses. Numerical example in a 10-unit 39-bus power system with 2 UPFC's shows that the performance index can be very much reduced by operating many UPFC's with the reactive powers for the optimal voltage profile proposed in this paper.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.624-629
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• 1993

In this paper, a model reference adaptive control scheme is applied to the normal acceleration controller for missiles with nonminimum-phase characteristics. The proposed scheme has an auxiliary compensator, an identifier of plant parameters and a feedback control law. First, plant parameters are estimated by the identifier and based the parameter estimates the coefficients of the compensator are calculated so that the estimated plant model with the compensator becomes minimum-phase. In this calculation, Nehari Algorithm is used. Parameters of the control law are then updated so that the extended plant model follows the given reference model. It is shown that the performance of the designed controller is satisfied via computer simulations.

• Industrial Engineering and Management Systems
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• v.11 no.3
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• pp.233-240
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• 2012

The implementation of business process management (BPM) systems in large number of business organizations transforms BPM system into such a level of maturity and tends to collect large repositories of business process (BP) models. This issue encourages BP flexibility that leads to a large number of process variants derived from the same model, but differing in structure, to be stored in the large repositories of BP models. Therefore, the repositories may include thousands of activities and related business objects with variation of requirements and quality of service. It is a common practice to customize processes from reference processes or templates in order to reduce the time and effort required to design and deploy processes on all levels. In order to address redundancy and underutilization problems, a generic process model, called as reference BP, is absolutely necessary to cover the best of process variants. This study aims to develop multiple-objective business process genetic algorithm (MOBPGA) to find a set of non-dominated (Pareto) solutions of business reference model to enhance conventional approach which considered only a single objective on creating BP reference model by using proximity score measurement. A mixed-integer linear program is constructed to evaluate performance of the proposed MOBPGA on small-scale problems by using standard measures for multiple-objective techniques. The results will show the viability of applying MOBPGA in terms of simultaneously maximizing proximity score measurement, minimizing total duration, and total costs of the selected reference model.

• Journal of Power Electronics
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• v.7 no.2
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• pp.159-173
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• 2007

In this paper, an intelligent sliding-mode position controller (ISMC) for achieving favorable decoupling control and high precision position tracking performance of permanent-magnet synchronous motor (PMSM) servo drives is proposed. The intelligent position controller consists of a sliding-mode position controller (SMC) in the position feed-back loop in addition to an on-line trained fuzzy-neural-network model-following controller (FNNMFC) in the feedforward loop. The intelligent position controller combines the merits of the SMC with robust characteristics and the FNNMFC with on-line learning ability for periodic command tracking of a PMSM servo drive. The theoretical analyses of the sliding-mode position controller are described with a second order switching surface (PID) which is insensitive to parameter uncertainties and external load disturbances. To realize high dynamic performance in disturbance rejection and tracking characteristics, an on-line trained FNNMFC is proposed. The connective weights and membership functions of the FNNMFC are trained on-line according to the model-following error between the outputs of the reference model and the PMSM servo drive system. The FNNMFC generates an adaptive control signal which is added to the SMC output to attain robust model-following characteristics under different operating conditions regardless of parameter uncertainties and load disturbances. A computer simulation is developed to demonstrate the effectiveness of the proposed intelligent sliding mode position controller. The results confirm that the proposed ISMC grants robust performance and precise response to the reference model regardless of load disturbances and PMSM parameter uncertainties.

• Journal of the Korean Society for information Management
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• v.38 no.1
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• pp.221-242
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• 2021

This study applied reference metadata recognition using bidirectional GRU-CRF model based on pre-trained language model. The experimental group consists of 161,315 references extracted by 53,562 academic documents in PDF format collected from 40 journals published in 2018 based on rules. In order to construct an experiment set. This study was conducted to automatically extract the references from academic literature in PDF format. Through this study, the language model with the highest performance was identified, and additional experiments were conducted on the model to compare the recognition performance according to the size of the training set. Finally, the performance of each metadata was confirmed.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2299-2301
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• 2000

In this paper, the design of PID controller using Neural networks for the control of non-linear system is presented. First, non-linear system is identified using BPN(Backpropagation Network) algorithm. This identified model is connected to the PID controller and the parameters of PID controller are updated to the direction of reducing the difference between the identified model output and model reference output in arbitrary input signal. Therefore, identified model output tracks the model reference output in an acceptable error range and the parameters of controller are updated adaptively. The output of the system has a good performance in case of both noisy and noiseless model reference and we can control the system stable in off-line when the dynamics of the system is changed.

• The KSFM Journal of Fluid Machinery
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• v.10 no.3 s.42
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• pp.9-16
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• 2007

Deterioration of surface of turbomachinery blades occurs in course of time due to many factors and hence reduces the performance of the machine. In this paper, the effects of surface roughness of transonic axial compressor blade on performance are studied considering a reference blade and a shape distorted (optimized) blade. Optimal blade is designed considering sweep and lean. Baldwin-Lomax turbulence model is used for flow field analysis and Cebeci-Smith roughness model is formulated for roughness modeling. It is found that, as the surface roughness increases, adiabatic efficiency, total temperature ratio and total pressure ratio decrease while Mach number increases. Performance deterioration is more severe in case of distorted blade as compared to reference blade.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.570-572
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• 2005

This paper is proposed adaptive hybrid artificial intelligent(HAI) controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network(FNN) controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.