• ETRI Journal
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• v.28 no.6
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• pp.799-802
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• 2006

Capacity has always been a major concern in wireless networks. This letter studies the impact of mobility on the overall system capacity in wireless cellular networks. In this letter, we present a simple system model which we developed to capture the inherent relationships among system capacity, new call blocking probability, handoff dropping probability, call terminating probability, and bandwidth utilization rate. We investigate the complex relationship between mobility and capacity-related parameters. Through simulation, we demonstrate that mobility has a significant impact on capacity and is reversely proportional to the bandwidth reserved for handoff traffic.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1221-1226
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• 2006

The use of Artificial Neural Networks (ANN) has received increasing attention in the analysis and prediction of financial time series. Stationarity of the observed financial time series is the basic underlying assumption in the practical application of ANN on financial time series. In this paper, we will investigate whether it is feasible to relax the stationarity condition to non-stationary time series. Our result discusses the range of complexities caused by non-stationary behavior and finds that overfitting by ANN could be useful in the analysis of such non-stationary complex financial time series.

• Journal of the Korea Society for Simulation
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• v.14 no.4
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• pp.55-68
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• 2005

The advancement in semiconductor technology is leading toward smaller critical dimension designs and larger wafer manufactures. Due to such phenomena, semiconductor industry is in need of an accurate control of the process. Photolithography is one of the key processes where the pattern of each layer is formed. In this process, precise superposition of the current layer to the previous layer is critical. Therefore overlay parameters of the semiconductor photolithography process is targeted for this research. The complex relationship among the input parameters and the output metrologies is difficult to understand and harder yet to model. Because of the superiority in modeling multi-nonlinear relationships, neural networks is used for the simulator modeling. For training the neural networks, conjugate gradient method is employed. An experiment is performed to evaluate the performance among the proposed neural network simulator, stepwise regression model, and the currently practiced prediction model from the test site.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.4
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• pp.551-560
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• 1998

This paper presents a neural network model representing complex hydro-thermo-dynamic characteristics of a steam generator in nuclear power plants. The key modeling processes include training data gathering process, analysis of system dynamics and determining of the neural network structure, training process, and the final process for validation of the trained model. In this paper, we suggest a training data gathering method from an unstable steam generator so that the data sufficiently represent the dynamic characteristics of the plant over a wide operating range. In addition, we define the inputs and outputs of neural network model by analyzing the system dimension, relative degree, and inputs/outputs of the plant. Several types of neural networks are applied to the modeling and training process. The trained networks are verified by using a class of test data, and their performances are discussed.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.15-15
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• 2000

In the past couple of years, there has been increasing interest in the fusion of neural networks and fuzzy logic. Most of the existing fused models have been proposed to implement different types of fuzzy reasoning mechanisms and inevitably they suffer from the dimensionality problem when dealing with complex real-world problem. To overcome the problem, we propose the self-organizing networks with activation nodes based on fuzzy inference and polynomial function. The proposed model consists of two parts, one is fuzzy nodes which each node is operated as a small fuzzy system with fuzzy implication rules, and its fuzzy system operates with Gaussian or triangular MF in Premise part and constant or regression polynomials in consequence part. the other is polynomial nodes which several types of high-order polynomials such as linear, quadratic, and cubic form are used and are connected as various kinds of multi-variable inputs. To demonstrate the effectiveness of the proposed method, time series data for gas furnace process has been applied.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.8
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• pp.1914-1927
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• 1996

Banyan networks have a number of applications in the area of computer and communications. While several analytical models have been proposed for the performance evaluation of Banyan networks, they are either not very accurate of too complex to be generalized. In this paper a new model for evaluating multibuffered MINs with 2*2 switching elements is proposed. the proposed model is very accurate for any size and traffic condition. It is also simple and can be easily generalized.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.302-305
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• 1996

In this paper, an optimal identification method using fuzzy-neural networks is proposed for modeling of nonlinear complex systems. The proposed fuzzy-neural modeling implements system structure and parameter identification using the intelligent schemes together with optimization theory, linguistic fuzzy implication rules, and neural networks(NNs) from input and output data of processes. Inference type for this fuzzy-neural modeling is presented as simplified inference. To obtain optimal model, the learning rates and momentum coefficients of fuzz-neural networks(FNNs) and parameters of membership function are tuned using genetic algorithm(GAs). For the purpose of its application to nonlinear processes, data for route choice of traffic problems and those for activated sludge process of sewage treatment system are used for the purpose of evaluating the performance of the proposed fuzzy-neural network modeling. The show that the proposed method can produce the intelligence model w th higher accuracy than other works achieved previously.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.185-188
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• 1997

In this paper, we execute identification, linearization, and control of a nonlinear system using recurrent neural networks. In general nonlinear control system become complex because of nonlinearity and uncertainty. And though we compose nonlinear control system based on the model, it is difficult to get good control ability. So we identify the nonlinear control system using the recurrent neural networks and execute feedback linearization of identified model, In this process we choose the optional linear system, and the system which will have to be feedback linearized if trained to follow the linearity between input and output of the system we choose. We the feedback linearized system by applying standard linear control strategy and simulation. And we evaluate the effectiveness by comparing the result which is linearized theoretically.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.1
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• pp.119-128
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• 2013

The spotlight is thrown on the ubiquitous technology these days, and diverse management technologies are proposed to efficiently manage the networks in the ubiquitous environment. Because each network has its unique management technology, the integrated management of complex networks is a very difficult challenge. In this paper, an integrated network management methodology is proposed to ensure the efficient management of different networks using ontology. Although the proposed integrated network management methodology is quite simple, the definition of this methodology is essential for the integrated network management. Using the $Prot\acute{e}g\acute{e}$ to Ontology development, the terms for the integrated network management were defined, along with the OWL and relevant rules, and several methods were implemented according to the proposed methodology. The process in this paper is considered essential for the network expansion and multiple network management.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1378-1381
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• 2002

This paper describes a control network which has a new node structure in the LonWorks networks. The proposed node structure is applicable to flexible and more complex applications which are impossible in the conventional Lonworks node structure. We implemented a node in order to evaluate the proposed control networks and verified the commercial feasibility and compatibility by experimenting the implemented node in the conventional Lonworks control networks.