• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.9
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• pp.809-818
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• 1991

We investigate the onsiderations of design issues for a bradband ATM network. Three kimds of network design requirement are considered ; user grade-of-service(GOS) requirements. Network manager requirements, and system designer requirements. In this work, e are focusing on the balancing problems among performance measures. We suggest that design parameters for a bradband ATM echange would be tuned within the acceptable sets by a layering concept on the peformance objectives.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.231-234
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• 1993

This paper deals with the design of an adaptive controller using neural network. We present RBFMLP Neural Network which consists of serial-connected two networks - Radial Basis Function Network and Multi Layer Perceptron, and then design a controller based on proposed networks with the adaptive control system structure, The plant and parameters of the controller are identified by the neural networks. We use the dynamic backpropagation algorithm for the learning of networks. Simulations represent the superiorities of the proposed network and the controller.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.621-623
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• 2000

In this paper, the effective design method of the predictive controller using fuzzy neural networks(FNNs) is presented for the Intelligent control of chaotic nonlinear systems. In our design method of controller, predictor parameters are tuned by the error value between the actual output of a chaotic nonlinear system and that of a fuzzy neural network model. And the parameters of predictive controller using fuzzy neural network are tuned by the gradient descent method which uses control error value between the actual output of a chaotic nonlinear system and the reference signal. In order to evaluate the performance of our controller, it is applied to the Duffing system which are the representative continuous-time chaotic nonlinear systems and the Henon system which are representative discrete-time chaotic nonlinear systems.

• Journal of Power Electronics
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• v.18 no.1
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• pp.289-299
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• 2018

Electric-field coupled power transfer (ECPT) systems employ a high frequency electric field as an energy medium to transfer power wirelessly. Existing ECPT systems have made great progress in terms of increasing the transfer distance. However, the topologies of these systems are complex, and the transfer characteristics are very sensitive to variations in the circuit parameters. This paper proposes an ECPT system with a double-sided LC network, which employs a parallel LC network on the primary side and a series LC network on the secondary side. With the same transfer distance and output power, the proposed system is simpler and less sensitive than existing systems. The expression of the optimal driving voltage for the coupling structure and the characteristics of the LC networks are also analyzed, including the transfer efficiency, parameter sensitivity and total harmonic distortion. Then, a design method for the system parameters is provided according to these characteristics. Simulations and experiments have been carried out to verify the system properties and the design method.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.18 no.6
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• pp.681-689
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• 2009

Disk grinding was often applied to deburring process in order to enhance the final product quality. Inherent chamfering capability of the flexible disk grinding process in the early stage was analyzed with respect to various process parameters including workpiece length, wheel speed, depth of cut and feed. Initial chamfered edge defined as arc zone was characterized with local radius of curvature. Averaged radius and arc zone ratio was well evaluated using neural network system. Additional neural network analysis adding workpiece length showed enhance performance in predicting arc zone ratio and curvature radius with reduced error rate. A process condition design parameter was estimated using remaining input and output parameters with the prediction error rate lower than 2.0% depending on the relevant input parameter combination and neural network structure composition.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.1
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• pp.22-30
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• 2001

This paper describes a neural network based fuzzy PID control scheme. The PID controller is being widely used in industrial applications. However, it is difficult to determine the appropriated PID gains in nonlinear systems and systems with long time delay and so on. In this paper, we re-analyzed the fuzzy controller as conventional PID controller structure, and proposed a neural network based self tuning fuzzy PID controller of which output gains were adjusted automatically. The tuning parameters of the proposed controller were determined on the basis of the conventional PID controller parameters tuning methods. Then they were adjusted by using proposed neural network learning algorithm. Proposed controller was simple in structure and computational burden was small so that on-line adaptation was easy to apply to. The experiment on the magnetic levitation system, which is known to be heavily nonlinear, showed the proposed controller's excellent performance.

• The KSFM Journal of Fluid Machinery
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• v.1 no.1 s.1
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• pp.41-48
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• 1998

This study was carried out to investigate the effect of design parameters on the volume flow-rate and the noise level and to finally find the optimal design variables. Eighteen cross-flow fans were designed by the method of orthogonal array, and the flow-rate and the noise level were measured. These data were analyzed by the neural network system. The effects of eight design variables(scroll exit angle, scroll arc length et al.) on the fan performance and the noise level were valuated and discussed. This experiment shows that the design solutions suggested by neural network system may increase its volume flow-rate and reduce noise simultaneously.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.6
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• pp.587-594
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• 2010

This paper describe the optimal design of geodetic network by analytical technique based on the quality criteria of network. We described an example of geodetic network design taking into account the precision, reliability and robustness that are the main criteria of network design. The main goal of this paper is to evaluate the criteria to design the geodetic network coinciding with the criteria of high precision(error ellipse, 2DRMS, CEP), reliability(internal and external reliability) and robustness(maximum shear strain, principal strain, dilatation). The network design parameters computed in this study show that precision and reliability has not much improved by about 2% and 3%, respectively, than the observed network, while robustness has much improved by about 3, 100%. It also shown that maximum errors of precision, reliability and robustness were reduced by 5%, 7% and 16,957%, respectively.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.110-115
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• 2001

Today, effective utilization of sophisticated networks greatly influences the activities of a business, making performance evaluations of computer network systems a necessity, We have developed a special computer network simulator capable of automatically generating a model based on data accumulated by a network analyzer to guide the user in selecting ideal parameters. The simulator was developed to provide user-friendly analysis for engineers involved in the actual network design. This paper gives an overview of the simulator and describes an example application of evaluating a network design that anticipates the future increase in traffic for a company introducing voice over frame relay (VoFR) into a wide area network (WAW).

• Journal of Electrical Engineering and Technology
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• v.7 no.4
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• pp.636-645
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• 2012

This study introduces an information granular-based fuzzy radial basis function neural networks (FRBFNN) based on multiobjective optimization and weighted least square (WLS). An improved multiobjective space search algorithm (IMSSA) is proposed to optimize the FRBFNN. In the design of FRBFNN, the premise part of the rules is constructed with the aid of Fuzzy C-Means (FCM) clustering while the consequent part of the fuzzy rules is developed by using four types of polynomials, namely constant, linear, quadratic, and modified quadratic. Information granulation realized with C-Means clustering helps determine the initial values of the apex parameters of the membership function of the fuzzy neural network. To enhance the flexibility of neural network, we use the WLS learning to estimate the coefficients of the polynomials. In comparison with ordinary least square commonly used in the design of fuzzy radial basis function neural networks, WLS could come with a different type of the local model in each rule when dealing with the FRBFNN. Since the performance of the FRBFNN model is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules and the orders of the polynomials present in the consequent parts of the rules, we carry out both structural as well as parametric optimization of the network. The proposed IMSSA that aims at the simultaneous minimization of complexity and the maximization of accuracy is exploited here to optimize the parameters of the model. Experimental results illustrate that the proposed neural network leads to better performance in comparison with some existing neurofuzzy models encountered in the literature.