• Journal of IKEEE
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• v.5 no.1 s.8
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• pp.100-110
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• 2001

In this paper, we investigated several buffer management schemes for the design of shared-memory type ATM switches, which can enhance the utilization of switch resources and can support quality-of-service (QoS) functionalities. Our results show that dynamic threshold (DT) scheme demonstrate a moderate degree of robustness close to pushout(PO) scheme, which is known to be impractical in the perspective of hardware implementation, under various traffic conditions such as traffic loads, burstyness of incoming traffic, and load non-uniformity across output ports. Next, we considered buffer management strategies to support QoS functions, which utilize parameter values obtained via connection admission control (CAC) procedures to set tile threshold values. Through simulations, we showed that the buffer management schemes adopted behave well in the sense that they can protect regulated traffic from unregulated cell traffic in allocating buffer space. In particular, it was observed that dynamic partitioning is superior in terms of QoS support than virtual partitioning.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.2
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• pp.399-406
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• 2009

In this study, Polynomial Radial Basis Function Neural Network(pRBFNN) based on Fuzzy Inference System is designed and its parameters such as learning rate, momentum coefficient, and distributed weight (width of RBF) are optimized by means of Particle Swarm Optimization. The proposed model can be expressed as three functional module that consists of condition part, conclusion part, and inference part in the viewpoint of fuzzy rule formed in 'If-then'. In the condition part of pRBFNN as a fuzzy rule, input space is partitioned by defining kernel functions (RBFs). Here, the structure of kernel functions, namely, RBF is generated from HCM clustering algorithm. We use Gaussian type and Inverse multiquadratic type as a RBF. Besides these types of RBF, Conic RBF is also proposed and used as a kernel function. Also, in order to reflect the characteristic of dataset when partitioning input space, we consider the width of RBF defined by standard deviation of dataset. In the conclusion part, the connection weights of pRBFNN are represented as a polynomial which is the extended structure of the general RBF neural network with constant as a connection weights. Finally, the output of model is decided by the fuzzy inference of the inference part of pRBFNN. In order to evaluate the proposed model, nonlinear function with 2 inputs, waster water dataset and gas furnace time series dataset are used and the results of pRBFNN are compared with some previous models. Approximation as well as generalization abilities are discussed with these results.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.4
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• pp.224-231
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• 2012

In this paper, we introduce a fuzzy inference systems based on fuzzy c-means clustering algorithm for fuzzy modeling of nonlinear process. Typically, the generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, the fuzzy rules of fuzzy model are generated by partitioning the input space in the scatter form using FCM clustering algorithm. The premise parameters of the fuzzy rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the coefficient parameters of each rule are determined by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process.

• Structural Engineering and Mechanics
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• v.43 no.2
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• pp.179-197
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• 2012

This study presents the findings of the structural health monitoring and the real time system identification of one of the first large scale building instrumentations in Turkey for earthquake safety. Within this context, a thorough review of steps in the instrumentation, monitoring is presented and seismic performance evaluation of structures using both nonlinear pushover and nonlinear dynamic time history analysis is carried out. The sensor locations are determined using the optimal sensor placement techniques used in NASA for on orbit modal identification of large space structures. System identification is carried out via the stochastic subspace technique. The results of the study show that under ambient vibrations, stocky buildings can be substantially stiffer than what is predicted by the finite element models due to the presence of a large number of partitioning walls. However, in a severe earthquake, it will not be safe to rely on this resistance due to the fact that once the partitioning walls crack, the bare frame contributes to the lateral stiffness of the building alone. Consequently, the periods obtained from system identification will be closer to those obtained from the FE analysis. A technique to control the validity of the proportional damping assumption is employed that checks the presence of phase difference in displacements of different stories obtained from band pass filtered records and it is confirmed that the "proportional damping assumption" is valid for this structure. Two different techniques are implemented for identifying the influence of the soil structure interaction. The first technique uses the transfer function between the roof and the basement in both directions. The second technique uses a pre-whitening filter on the data obtained from both the basement and the roof. Subsequently the impulse response function is computed from the scaled cross correlation between the input and the output. The overall results showed that the structure will satisfy the life safety performance level in a future earthquake but some soil structure interaction effects should be expected in the North South direction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2072-2078
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• 2011

In photovoltaic(PV) system, for obtaining maximum efficiency of solar power systems, the solar tracking system must be controlled to match position of the sun. In this paper, we design the solar tracking system to track movement of the sun using CdS sensor modules and to determine direction of the sun under shadow of directions. In addition, for an intelligent computation in tracking of the sun, a fuzzy controller is allocated to space avaliable for splitting area of fuzzy part for the fuzzy input space(grid-type fuzzy partition) in which a fuzzy grid partition divides fuzzy rules bases. As well, a simple model of solar tracking system is designed by two-axis motor control systems and the 8-direction sensor module that can measure shadow from CdS sensor modules by matching of axis of CdS modules and PV panels. We demonstrate this systems is effective for fixed location and moving vessels and our fuzzy controller can track the satisfactorily.

• Nuclear Engineering and Technology
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• v.54 no.2
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• pp.608-616
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• 2022

Power peaking factors (PPF) is an important parameter for safe and efficient reactor operation. There are several methods to calculate the PPF at TRIGA research reactors such as MCNP and TRIGLAV codes. However, these methods are time-consuming and required high specifications of a computer system. To overcome these limitations, artificial intelligence was introduced for parameter prediction. Previous studies applied the neural network method to predict the PPF, but the publications using the ANFIS method are not well developed yet. In this paper, the prediction of PPF using the ANFIS was conducted. Two input variables, control rod position, and neutron flux were collected while the PPF was calculated using TRIGLAV code as the data output. These input-output datasets were used for ANFIS model generation, training, and testing. In this study, four ANFIS model with two types of input space partitioning methods shows good predictive performances with R² values in the range of 96%-97%, reveals the strong relationship between the predicted and actual PPF values. The RMSE calculated also near zero. From this statistical analysis, it is proven that the ANFIS could predict the PPF accurately and can be used as an alternative method to develop a real-time monitoring system at TRIGA research reactors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3515-3538
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• 2015

Data accuracy can be increased by detecting and removing the incorrect data generated in wireless sensor networks. By increasing the data accuracy, network lifetime can be increased parallel. Network lifetime or operational time is the time during which WSN is able to fulfill its tasks by using microcontroller with on-chip memory radio transceivers, albeit distributed sensor nodes send summary of their data to their cluster heads, which reduce energy consumption gradually. In this paper a powerful algorithm using proactive fuzzy system is proposed and it is a mixture of fuzzy logic with comparative correlation techniques that ensure high data accuracy by detecting incorrect data in distributed wireless sensor networks. This proposed system is implemented in two phases there, the first phase creates input space partitioning by using robust fuzzy c means clustering and the second phase detects incorrect data and removes it completely. Experimental result makes transparent of combined correlated fuzzy system (CCFS) which detects faulty readings with greater accuracy (99.21%) than the existing one (98.33%) along with low false alarm rate.

• Structural Monitoring and Maintenance
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• v.6 no.4
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• pp.317-346
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• 2019

Performance assessment of pavements proves useful, in terms of handling the ride quality, controlling the travel time of vehicles and adequate maintenance of pavements. Roughness profiles provide a good measure of the deteriorating condition of the pavement. For the accurate estimates of pavement roughness from dynamic vehicle responses, vehicle parameters should be known accurately. Information on vehicle parameters is uncertain, due to the wear and tear over time. Hence, condition monitoring of pavement requires the identification of pavement roughness along with vehicle parameters. The present study proposes a scheme which estimates the roughness profile of the pavement with the use of accurate estimates of vehicle parameters computed in parallel. Pavement model used in this study is a two-layer Euler-Bernoulli beam resting on a nonlinear Pasternak foundation. The asphalt topping of the pavement in the top layer is modeled as viscoelastic, and the base course bottom layer is modeled as elastic. The viscoelastic response of the top layer is modeled with the help of the Burgers model. The vehicle model considered in this study is a half car model, fitted with accelerometers at specified points. The identification of the coupled system of vehicle-pavement interaction employs a coupled scheme of an unbiased minimum variance estimator and an optimization scheme. The partitioning of observed noisy quantities to be used in the two schemes is investigated in detail before the analysis. The unbiased minimum variance estimator (MVE) make use of a linear state-space formulation including roughness, to overcome the linearization difficulties as in conventional nonlinear filters. MVE gives estimates for the unknown input and fed into the optimization scheme to yield estimates of vehicle parameters. The issue of ill-posedness of the problem is dealt with by introducing a regularization equivalent term in the objective function, specifically where a large number of parameters are to be estimated. Effect of different objective functions is also studied. The outcome of this research is an overall measure of pavement condition.