• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.3
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• pp.170-176
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• 2004

MR dampers show highly nonlinear and histeretic dynamic behavior. Therefore, for a vehicle dynamic simulation with MR dampers, this dynamic characteristics should be accurately reflected in the damper model. In this paper, an artificial neural network technique was developed for modeling MR dampers. This MR damper model was successfully verified through a random input forcing test. This MR damper model can be used for semi-active suspension vehicle dynamics and control simulations with practical accuracy.

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1213-1220
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• 2022

The Emergency Diesel Generator (EDG) is a critical and essential part of the Nuclear Power Plant (NPP). Due to past catastrophic disasters, critical systems of NPP like EDG are designed to meet high dependability requirements. Therefore, we propose a framework for the dynamic reliability assessment using the Fault Tree and the Dynamic Bayesian Network. In this framework, the information of the component's failure probability is updated based on observed data. The framework is powerful to perform qualitative as well as quantitative analysis of the system. The validity of the framework is done by applying it on several NPP systems.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.18-23
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• 1990

In this paper, the dynamic modeling and a tip-position controller of a single-link flexible manipulator are developed. To design the controller of a flexible manipulator, at first, it is required to obtain the accurate dynamic model of manipulator describing both rigid motion and flexible vibration. For this purpose, FEM(Finite Element Method) and Lagrange approach are utilized to obtain the dynamic model. After obtaining the dynamic model of a single-link manipulator, a controller which computes the input torque to perform the desired trajectory is developed using neural network.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.1
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• pp.39-42
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• 2002

For the purpose of modeling EEG signal which has nonstationary and nonlinear dynamic characteristics, this paper propose a state feedback real time recurrent neural network model. The state feedback real time recurrent neural network is structured to have memory structure in the state of hidden layers so that it has arbitrary dynamics and ability to deal with time-varying input through its own temporal operation. For the model test, Mackey-Glass time series is used as a nonlinear dynamic system and the model is applied to the prediction of three types of EEG, alpha wave, beta wave and epileptic EEG. Experimental results show that the performance of the proposed model is better than that of other neural network models which are compared in this paper in some view points of the converging speed in learning stage and normalized mean square error for the test data set.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.4
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• pp.110-118
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• 2004

In this paper, a blackbox approach is carried out to model the nonlinear dynamic bushing model. One-axis durability test is performed to describe the mechanical behavior of typical vehicle elastomeric components. The results of the tests are used to develop an empirical bushing model with an artificial neural network. The back propagation algorithm is used to obtain the weighting factor of the neural network. Since the output for a dynamic system depends on the histories of inputs and outputs, Narendra's algorithm of ‘NARMAX’ form is employed in the neural network bushing module. A numerical example is carried out to verify the developed bushing model.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.421-424
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• 2003

This paper proposes the generalized predictive control(GPC) method of chaotic systems using a self-recurrent wavelet neural network(SRWNN). The reposed SRWNN, a modified model of a wavelet neural network(WNN), has the attractive ability such as dynamic attractor, information storage for later use. Unlike a WNN, since the SRWNN has the mother wavelet layer which is composed of self-feedback neurons, mother wavelet nodes of the SRWNN can store the past information of the network. Thus the SRWNN can be used as a good tool for predicting the dynamic property of nonlinear dynamic systems. In our method, the gradient-descent(GD) method is used to train the SRWNN structure. Finally, the effectiveness and feasibility of the SRWNN based GPC is demonstrated with applications to a chaotic system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.2
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• pp.1-8
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• 2019

Network externalities are essentially dynamic in that the value consumers feel about a product is affected by the size of the existing customer base that uses that product. However, existing studies on network externalities analyzed the effects of network externalities in a static way, not dynamic. In this study, unlike previous studies, the impact of network externalities on price competition in a vertically differentiated market is dynamically analyzed. To this end, a two-period duopoly game model was used to reflect the dynamic aspects of network externalities. Based on the game model, the Nash equilibria for price, sales volume, and revenue were derived and numerically analyzed. The results can be summarized as follows. First, if high-end product has strong market power, the high-end product vendor takes almost all benefits of the network externality. Second, when high-end product has strong market power, the low-end product will take over most of the initial sales volume increase. Third, when market power of high-end product is not strong, it can be seen that the effects of network externalities on the high and low-end products are generally proportional to the difference in quality. Lastly, if there exists a strong network externality, it is shown that the presence of low-end product can be more profitable for high-end product vendor. In other words, high-end product vendor has incentive to disclose some technologies for the market entrance of low-end product, even if it has exclusive rights to the technologies. In that case, however, it is shown that the difference in quality should be maintained significantly.

• Journal of Computing Science and Engineering
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• v.2 no.3
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• pp.249-273
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• 2008

Epidemic protocols have two fundamental assumptions. One is the availability of a mechanism that provides each node with a set of log(N) (fanout) nodes to gossip with at each cycle. The other is that the network size N is known to all member nodes. While it may be trivial to support these assumptions in small systems, it is a challenge to realize them in large open dynamic systems, such as peer-to-peer (P2P) systems. Technically, since the most fundamental parameter of epidemic protocols is log(N), without knowing the system size, the protocols will be limited. Further, since the network churn, frequently observed in P2P systems, causes rapid membership changes, providing a different set of log(N) at each cycle is a difficult problem. In order to support the assumptions, the fanout nodes should be selected randomly and uniformly from the entire membership. This paper investigates one possible solution which addresses both problems; providing at each cycle a different set of log(N) nodes selected randomly and uniformly from the entire network under churn, and estimating the dynamic network size in the number of nodes. This solution improves the previously developed distributed algorithm called Shuffle to deal with churn, and utilizes the Shuffle infrastructure to estimate the dynamic network size. The effectiveness of the proposed solution is evaluated by simulation. According to the simulation results, the proposed algorithms successfully handle network churn in providing random log(N0 fanout nodes, and practically and accurately estimate the network size. Overall, this work provides insights in designing epidemic protocols for large scale open dynamic systems, where the protocols behave autonomically.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.465-468
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• 2007

An intelligent home network system using low-power and low-cost sensor nodes was designed and implemented. In Intelligent Home Network System, active home appliances control is composed of RSSI (Received Signal Strength Indicator) based user indoor location tracking, dynamic multi-hop routing, and learning integration remote-control. Through the remote-control learning, home appliances can be controlled in wireless network environment. User location information for intelligent service is calculated using RSSI based Triangle measurement method, and then the received location information is passed to Smoothing Algorithm to reduce error rate. In order to service Intelligent Home Network, moreover, the sensor node is designed to be held by user. The gathered user data is transmitted through dynamic multi-hop routing to server, and real-time user location & environment information are displayed on monitoring program.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.195-198
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• 2000

We propose a new wavelet neural network structure, for which we apply new recurrent nodes to the network, in this paper for the dynamic system identification and control. We will construct the wavelet neural network by using wavelet frame function. The function does not have the best approximation property, but it may be possible to apply some modification to the structure of the network because the constriction of orthogonality is loosened a little. This wavelet neural network we propose can obtain previous state information by its structure of the network without any addition of input, though the conventional wavelet network needs additional previous state input for the improvement of the dynamic performance. In numerical experience, the performance of the new wavelet neural network we propose in the nonlinear system with uncertainity of parameter Is equal to that of the wavelet network which used the additional previous information input, superior to that of the conventional wavelet network.