• Earthquakes and Structures
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• v.15 no.4
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• pp.431-441
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• 2018

This study provides fragility-based assessment of seismic performance of reinforced concrete bridges. Seismic fragility curves were created using nonlinear analysis (NA) and artificial neural networks (ANNs). Nonlinear response history analyses were performed, in order to calculate the seismic performances of the bridges. To this end, 306 bridge-earthquake cases were considered. A multi-layered perceptron (MLP) neural network was implemented to predict the seismic performances of the selected bridges. The MLP neural networks considered herein consist of an input layer with four input vectors; two hidden layers and an output vector. In order to train ANNs, 70% of the numerical results were selected, and the remained 30% were employed for testing the reliability and validation of ANNs. Several structures of MLP neural networks were examined in order to obtain suitable neural networks. After achieving the most proper structure of neural network, it was used for generating new data. A total number of 600 new bridge-earthquake cases were generated based on neural simulation. Finally, probabilistic seismic safety analyses were conducted. Herein, fragility curves were developed using numerical results, neural predictions and the combination of numerical and neural data. Results of this study revealed that ANNs are suitable tools for predicting seismic performances of RC bridges. It was also shown that yield stresses of the reinforcements is one of the important sources of uncertainty in fragility analysis of RC bridges.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.2
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• pp.351-358
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• 1994

A probabilistic model and analysis methods to determine the means and variances of the velocity and acceleration in stochastically-defined planar pin jointed kinematic chains are presented. The presented model considers the effect of tolerances on link length and radial clearance and uncertainty of pin location as a net effect on the link's effective length. The determination of the mean values and variances of the output variables requires the calculation of sensitivities of secondary variables with respect to the random variables. It is shown that this computation is straightforward and can be accomplished by a conventional kinematic analysis package with minor modification. Thus, the concepts of tolerance and clearance have been captured by the model and analysis. The only input data are the nominal linkage model and statistical information. The "effective link length" model is shown to be applicable to both analytical solution and Monte Carlo simulation. The results from both methods are compared. This paper Ksolves the higher-order kinematic problems for the probabilistic design analysis of stochastically-defined mechanisms.echanisms.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.4
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• pp.391-397
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• 2011

Control of interior permanent magnet (IPMSM) is difficult because its nonlinearity and parameter uncertainty. In this paper, a fuzzy c-regression models clustering algorithm which is based on T-S fuzzy is used to model IPMSM with a series linear model and weight them by memberships. Lagrangian of constrained function is built for calculating clustering centers where training output data are considered. Based on these clustering centers, least square method is applied for T-S fuzzy linear model parameters. As a result, IPMSM can be modeled as T-S fuzzy model for T-S fuzzy control of them.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2821-2823
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• 2002

In this paper we propose the Multi-FNN (Fuzzy-Neural Networks) for optimal identification modeling of complex system. The proposed Multi-FNNs is based on a concept of FNNs and exploit linear inference being treated as generic inference mechanisms. In the networks learning, backpropagation(BP) algorithm of neural networks is used to updata the parameters of the network in order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM(Hard C-Means)clustering algorithm which carry out the input-output dat a preprocessing function and Genetic Algorithm which carry out optimization of model The HCM clustering method is utilized to determine the structure of Multi-FNNs. The parameters of Multi-FNN model such as apexes of membership function, learning rates, and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization abilities of the model. NOx emission process data of gas turbine power plant is simulated in order to confirm the efficiency and feasibility of the proposed approach in this paper.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.1
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• pp.77-84
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• 2018

The turbine flowmeter is selected for high precision and reproducibility at the time of flow rate measurement but causes various uncertainty factors of measurement in the difference between the standard environmental condition at calibration and the environmental condition at the site. Also, a reliable interpolation method is required for use in sections other than calibrated measurement values. Therefore, in this paper, in order to improve the reliability of the flow rate measurement, we designed and manufactured a device that accurately measures the output signal of the turbine flowmeter, interpolates the value of the calibrated result value, and corrects the temperature change in real time We confirmed the reliability of the measurement at the site to carry out the performance verification.

• Computational Structural Engineering
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• v.2 no.4
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• pp.89-98
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• 1989

In order to take account of the statistical properties of random variables used in the structural analysis, the conventional approach usually adopts the safety factor based on past experiences for the qualitative assessment of structural safety problem. Recently, new approach based on the probabilistic concept has been applied to the assessment of structural safety in order to circumvent the difficulties of the conventional approach in choosing the appropriate safety factor. Thus, computer program called "Probabilistic finite element method" is developed by incorporating the probabilistic concept into the conventional matrix method in order to investigate the effects of the random variables on the final output of the structural analysis. From the comparison of some examples, it can be concluded that the PFEM developed in this study deals consistently with the uncertainty of random variables and provides the rational tool for the assessment of structural safety of plane frame.

• Journal of Korean Institute of Industrial Engineers
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• v.43 no.2
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• pp.90-99
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• 2017

As the importance of R&D has increased, there have been various efforts to apply the quality management principles and tools to R&D activities in order to manage them effectively. The R&D sector differs from other value chains, so it may be difficult to apply quality management without proper considerations of R&D characteristics. This study describes the characteristics of R&D as high uncertainty and risk, diversity of R&D types, project-based activities, importance of strategic goals and business models, and importance of intangible assets. Three well accepted R&D quality management models are reviewed and implications for quality management and R&D characteristics are summarized. Based on these findings, the management targets of R&D quality management are classified into management level (organization, project) and management focus (process, output), and the contexts of R&D quality management are classified into R&D type (research, development) and market/customer requirement clarity (fluid, specific), and appropriate R&D quality management activities in each situations have been suggested.

• Journal of the Korean Magnetics Society
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• v.14 no.4
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• pp.138-142
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• 2004

Uncertainty and characteristics of the developed current sensor by means of two identically wound magnetic cores forming a ring like for measurement of a low DC current such as leakage current was described in this paper. This transducer consists of a sensor type of a current transformer, peak value detectors, a reference alternating low frequency voltage oscillator, precision measuring circuits to measure the output signals of sensor with harmonics, and can be measured up to 2 A at DC current. The resolution and sensitivity of the sensor were 0.1㎃ and 10㎷/㎃, respectively.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.2
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• pp.55-61
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• 2000

In this paper, we proposed a new PID tuning algorithm by the fuzzy set theory to improve the performance of the PID controller. The new tuning algorithm for the PID controller has the initial value of parameter Kc, $\tau$I, $\tau$D by the Ziegler-Nichols formula using the ultimate gain and ultimate period from a relay tuning experiment. We get error and error change of plant output correspond to the initial value and new proportion gain(Kc) and integral time($\tau$I) from fuzzy tunner. This fuzzy tuning algorithm for PID controller considerably reduced overshoot and rise time compare to any other PID controller tuning algorithms. In real parametric uncertainty systems, the PID controller with Fuzzy auto-tuning give appreciable improvement in the performance. The significant properties of this algorithm is shown by simulation In this paper, we proposed a new PID algorithm by the fuzzy set theory to improve the performance of the PID controller.

• International Journal of Ocean System Engineering
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• v.3 no.2
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• pp.50-60
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• 2013

This paper presents a novel approach to the design of an adaptive fuzzy sliding mode controller for depth control of an autonomous underwater vehicle (AUV). So far, AUV's dynamics are highly nonlinear and the hydrodynamic coefficients of the vehicles are difficult to estimate, because of the variations of these coefficients with different operating conditions. These kinds of difficulties cause modeling inaccuracies of AUV's dynamics. Hence, we propose an adaptive fuzzy sliding mode control with novel fuzzy adaptation technique for regulating vertical positioning in presence of parametric uncertainty and disturbances. In this approach, two fuzzy approximator are employed in such a way that slope of the linear sliding surface is updated by first fuzzy approximator, to shape tracking error dynamics in the sliding regime, while second fuzzy approximator change the supports of the output fuzzy membership function in the defuzzification inference module of fuzzy sliding mode control (FSMC) algorithm. Simulation results shows that, the reaching time and tracking error in the approaching phase can be significantly reduced with chattering problem can also be eliminated. The effectiveness of proposed control strategy and its advantages are indicated in comparison with conventional sliding mode control FSMC technique.