• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.126-131
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• 2009

This study was carried out for the reliability of PD(Partial Discharge) pattern recognition. For the pattern recognition, the database for PD was established by use of self-designed insulation defects which occur and were mostly critical in GIS(Gas Insulated Switchgear). The acquired database was analyzed to distinguish patterns by means of PRPD(Phase Resolved Partial Discharge) method and stored to the form with to unite the average amplitude of PD pulse and the number of PD pulse as the input data of neural network. In order to prove the performance of genetic algorithm combined with neural network, the neural networks with trial-and-error method and the neural network with genetic algorithm were trained by same training data and compared to the results of their pattern recognition rate. As a result, the recognition success rate of defects was 93.2% and the neural network train process by use of trial-and-error method was very time consuming. The recognition success rate of defects, on the other hand, was 100% by applying the genetic algorithm at neural network and it took a relatively short time to find the best solution of parameters for optimization. Especially, it could be possible that the scrupulous parameters were obtained by genetic algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.355-360
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• 2002

To calibrate a non-redundantly actuated parallel mechanism, one can find actual kinematic parameters by means of geometrical constraint of the mechanism's kinematic structure and measurement values. However, the calibration algorithm for a non-redundant case does not apply fur a redundantly actuated parallel mechanism, because the angle error of the actuating joint varies with position and the geometrical constraint fails to be consistent. Such change of joint angle error comes from constraint torque variation with each kinematic pose (meaning position and orientation). To calibrate a redundant parallel mechanism, one therefore has to consider constraint torque equilibrium and the relationship of constraint torque to torsional deflection, in addition to geometric constraint. In this paper, we develop the calibration algorithm fir a redundantly actuated parallel mechanism using these three relationships, and formulate cost functions for an optimization algorithm. As a case study, we executed the calibration of a 2-DOF parallel mechanism using the developed algorithm. Coordinate values of tool plate were measured using a laser ball bar and the actual kinematic parameters were identified with a new cost function of the optimization algorithm. Experimental results showed that the accuracy of the tool plate improved by 82% after kinematic calibration in a redundant actuation case.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.12
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• pp.3275-3295
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• 2012

In this paper, we analyze the performance of an amplify-and-forward (AF) two-way relaying system, where two sources exchange information via the aid of an intermediate relay that is selected among multiple relays according to max-min criterion. We consider a practical scenario, where one source-relay link undergoes Rician fading, and the other source-relay link is subject to Rayleigh fading. To be specific, we derive a tight lower bound for the outage probability. From this lower bound, the asymptotic outage probability and average symbol error rate (SER) expressions are derived to gain insight into the system performance at high signal-to-noise ratio (SNR) region. Furthermore, we investigate the optimal power allocation (PA) with fixed relay location (RL), optimal RL with fixed PA and joint optimization of PA and RL to minimize the outage probability and average SER. The analytical expressions are verified through Monte Carlo simulations, where the positive impact of Rician factor on the system performance is also illustrated. Simulation results also validate the effectiveness of the proposed PA and relay positioning schemes.

• Journal of Drive and Control
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• v.15 no.4
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• pp.74-80
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• 2018

A damper is a hydraulic device designed to absorb or eliminate shock impulses which is acting on the sprung mass of vehicle. It converting the kinetic energy of the shock into another form of energy, typically heat. In a vehicle, a damper reduce vibration of car, leading to improved ride comfort and running stability. Therefore, a damper is one of the most important components in a vehicle suspension system. Conventionally, the design process of vehicle suspensions has been based on trial and error approaches, where designers iteratively change the values of the design variables and reanalyze the system until acceptable design criteria are achieved. Therefore, the ability to tune a damper properly without trial and error is of great interest in suspension system design to reduce time and effort. For this reason, a many previous researches have been done on modeling and simulation of the damper. In this paper, we have conducted optimal design process to find optimal design parameters of damping force which minimize a acceleration of sprung mass for a given suspension system using genetic algorithm.

• Steel and Composite Structures
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• v.39 no.1
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• pp.81-93
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• 2021

Splitting tensile strength (STS) is an important mechanical parameter of concrete. This study offers novel methodologies for the early prediction of this parameter. Artificial neural network (ANN), which is a leading predictive method, is synthesized with two metaheuristic algorithms, namely atom search optimization (ASO) and equilibrium optimizer (EO) to achieve an optimal tuning of the weights and biases. The models are applied to data collected from the published literature. The sensitivity of the ASO and EO to the population size is first investigated, and then, proper configurations of the ASO-NN and EO-NN are compared to the conventional ANN. Evaluating the prediction results revealed the excellent efficiency of EO in optimizing the ANN. Accuracy improvements attained by this algorithm were 13.26 and 11.41% in terms of root mean square error and mean absolute error, respectively. Moreover, it raised the correlation from 0.89958 to 0.92722. This is while the results of the conventional ANN were slightly better than ASO-NN. The EO was also a faster optimizer than ASO. Based on these findings, the combination of the ANN and EO can be an efficient non-destructive tool for predicting the STS.

• Computers and Concrete
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• v.28 no.6
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• pp.635-642
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• 2021

In this paper, soft learning techniques are used to predict the ultimate torsional capacity of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. Soft computing techniques, namely Artificial Neural Network, trained by various back propagation algorithms, and Particle Swarm Optimization (PSO) algorithm, have been used to model and predict the torsional strength of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. The performance of each model has been evaluated by using statistical parameters such as coefficient of determination (R²), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The hybrid PSO NN model resulted in an R² of 0.9292 with an RMSE of 5.35 for training and an R² of 0.9328 with an RMSE of 4.57 for testing. Another model, ANN BP, produced an R² of 0.9125 with an RMSE of 6.17 for training and an R² of 0.8951 with an RMSE of 5.79 for testing. The results of the PSO NN model were in close agreement with the experimental values. Thus, the PSO NN model can be used to predict the ultimate torsional capacity of RC beams strengthened with FRP with greater acceptable accuracy.

• Journal of Biosystems Engineering
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• v.12 no.2
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• pp.38-43
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• 1987

An experimental study was conducted to develop a thin-layer rewetting equation of short grain rough rice of Akihikari variety. Four thin-layer rewetting equations were experimentally determined from $25^{\circ}C$ to $45^{\circ}C$ and 70%RH to 85%RH conditions. Diffusion, Henderson, Page, and Thompson equations widely used as thin-layer drying equations were selected. Experimental data were fitted to these equations using linear regression analysis except diffusion equation. The diffusivity in the diffusion equation was determined by optimization method. Four equations were highly significant. In order to compare the goodness of fit of each equation, the error mean square of each equawas calculated. The diffusion model was not a very good model because the error mean square was very large. The other three models showed the same level or error mean square and could predict satisfactorily the rewetting rate or short grain rough rice.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.11a
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• pp.91-98
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• 2008

Hypoid gears are widely used in rear drive and 4WD vehicle axles. Investigation of their sensitivity to deflections is one of the most important aspects of their design and optimization procedures. The deflection test is performed in the actual gear mounting using completely processed gear. This test should cover the fun operating range of gear loads from no load to peak load. Under peak load the contact pattern should extend to the tooth boundaries without showing a concentration of the contact pattern at any point on the tooth surface. Transmission error is tested on an axle assembly triaxial real car load condition.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.276-279
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• 2005

As the Ubiquitous generation approaches, the importance of the sensor data processing is growing. The data approximation scheme, one of the data processing methods, can be the key of sensor data processing, for it is related not only to the lifetime of sensors but also to the size of the storage. In this paper, we propose the Harmonic Wavelet transform which can minimize the relative error for given sensor data. Harmonic Wavelets use the harmonic mean as a representative which is the minimum point of the maximum relative error between two data values. In addition, Harmonic Wavelets retain the relative errors as wavelet coefficients so we can select proper wavelet coefficients that reduce the relative error more easily. We also adapt the greedy algorithm for local optimization to reduce the time complexity. Experimental results show the performance and the scalability of Harmonic Wavelets for sensor data.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.255-258
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• 2003

It is hard to centrol the wheeled mobile robot because of uncertainty of modeling, non-holonomic constraint and so on. To solve the problems, we design the controller of wheeled mobile robot based on fuzzy-neural network algorithm. In this paper, we should research the problem of classical controller for path-tracking algorithm and design of Fuzzy-Neural Network algorithm controller. Classical controller acquired different control value according to change of initial position and direction. In this control value having very difficult and having acquired a lot of trial and error Fuzzy is implemented to adaptive adjust control value by error and change of error and neural network is implemented to adaptive adjust the control gain during the optimization. The computer simulation shows that the proposed fuzzy-neural network controller is effective.