• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.371-376
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• 1994

This paper presents an on-line modeling method approach for the machine condition. the machine condition is continuously monitored with a sensor such as, a vibration, a current, an acoustic emission (AE) sensor. In this study, neural network modeling by radial basis function is designed for analysis a prediction error. An on-line learning algorithm is designed using the RLS(recursive least square) estimation and the existing clustering method of Kohonen neural network. Experimental results show that the proposed RBNN modeling is suitable for predicting simulated data.

• Korean Journal of Hydrosciences
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• v.2
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• pp.69-84
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• 1991

US NWS/NETWORK is applied for the analysis of the flood of July 11-15, 1981 through the Goan-Indogyo reach of the Han River. For the flood hydrography synthesis of the lateral inflows from the major tributaries into the main reach the Cleak method is employed. NETWORK coupled with the Clark method of hydrography synthesis simulated with a fair accuracy the oberved flood hydrograph at the downstream boundary of the routing reach. The dffect of SCS runoff curve number for fributary flood synthesis is evaluated. The characteristics of the station variations and time variations of the flood discharges in the reach is also analyzed.

• ETRI Journal
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• v.14 no.3
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• pp.67-74
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• 1992

Discrete variable을 갖는 Mean Field Theory(MFT) neural network은 이미 많은 combinatorial optimization 문제에 적용되어져 왔다. 본 논문에서는 이를 확장하여 continuous variable을 갖는 mean field annealing을 제안하고, 이러한 network에서 integral로 표현되는 spin average를 mean field에 기초하여 어렵지 않게 구할 수 있는 one-variable stochastic simulated annealing을 제안하였다. 이런 방법으로 multi-body problem을 single-body problem으로 바꿀 수 있었다. 또한 이 방법을 이용한 응용으로서 통계학에서 잘 알려진 문제중의 하나인 quantification analysis 문제에 적용하여 타당성을 보였다.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.9
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• pp.917-922
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• 2016

There are many researches related on the brain wave signals to monitor the state of human health. Especially, some patients in the medical hospital need to be monitored in case of emergencies such as a seizure, an epilepsy and so on. To support QoS of the brain wave network in the hospital is a vital issue and the Opnet simulator is used for this experiment. So the efficient network topology is required for the stability of the brain wave network service. The brain waves of the patients are collected from the sensor devices in the network. Two different sensor network topologies are suggested and simulated for the comparison of the network performance. One topology is localized and the other is non-localized network. The simulation is operated with the Opnet simulator.

• International journal of advanced smart convergence
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• v.5 no.1
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• pp.34-46
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• 2016

Networked Control System (NCS) has evolved in the past decade through the advances in communication technology. The problems involved in NCS are broadly classified into two categories namely network issues due to network and control performance due to system network. The network problems are related to bandwidth allocation, scheduling and network security, and the control problems deal with stability analysis and delay compensation. Various delays with variable length occur due to sharing a common network medium. Though most delays are very less and mostly neglected, the network induced delay is significant. It occurs when sensors, actuators, and controllers exchange data packet across the communication network. Networked induced delay arises from sensor to controller and controller to actuator. This paper presents an adaptive delay compensation process for efficient control. Though Smith predictor has been commonly used as dead time compensators, it is not adaptive to match with the stochastic behavior of network characteristics. Time delay adaptive compensation gives an effective control to solve dead time, and creates a virtual environment using the plant model and computed delay which is used to compensate the effect of delay. This approach is simulated using TrueTime simulator that is a Matlab Simulink based simulator facilitates co-simulation of controller task execution in real-time kernels, network transmissions and continuous plant dynamics for NCS. The simulation result is analyzed, and it is confirmed that this control provides good performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.795-797
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• 2009

Voice over Internet Protocol(VoIP) service becomes more and more popular nowadays. As such, it is developed over many kinds of network models, especially wireless networks. Mean Opinion Score(MOS) computes the QoS of VoIP service which should be supported by robust network environment. However, MANET is not stable enough to supply high MOS values for VoIP service. In this paper, VoIP service over MANET is simulated using ns-2(Network Simulation 2). In oder to get different MOS values in the results, we differentiate between network environments by adjusting the parameters of MANET.Through comparing the results we can know how to improve the QoS.

• Journal of the Korea Society of Computer and Information
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• v.9 no.3
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• pp.189-194
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• 2004

This paper proposes a Simulated Annealing(SA) algorithm for cluster-based Multicast Routing problems. Multicasting, the transmission of data to a group, can be solved from constructing multicast tree, that is. the whole network is partitioned to some clusters and the clusters are constructed by multicast tree. Multicast tree can be constructed by minimum-cost Steiner tree. In this paper, an SA algorithm is used in the minimum-cost Steiner tree. Especially, in SA, the cooling schedule is an important factor for the algorithm. Hence, in this paper, a cooling schedule is proposed for SA for multicast routing problems and analyzed the simulation results.

• Investigative Magnetic Resonance Imaging
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• v.6 no.2
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• pp.137-146
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• 2002

Purpose : This paper introduces an improved classification approach which adopts a deterministic relaxation method and an agglomerative clustering technique for the classification of MRI using neural network. The proposed approach can solve the problems of convergency to local optima and computational burden caused by a large number of input patterns when a neural network is used for image classification. Materials and methods : Application of Hopfield neural network has been solving various optimization problems. However, major problem of mapping an image classification problem into a neural network is that network is opt to converge to local optima and its convergency toward the global solution with a standard stochastic relaxation spends much time. Therefore, to avoid local solutions and to achieve fast convergency toward a global optimization, we adopt MFA to a Hopfield network during the classification. MFA replaces the stochastic nature of simulated annealing method with a set of deterministic update rules that act on the average value of the variable. By minimizing averages, it is possible to converge to an equilibrium state considerably faster than standard simulated annealing method. Moreover, the proposed agglomerative clustering algorithm which determines the underlying clusters of the image provides initial input values of Hopfield neural network. Results : The proposed approach which uses agglomerative clustering and deterministic relaxation approach resolves the problem of local optimization and achieves fast convergency toward a global optimization when a neural network is used for MRI classification. Conclusion : In this paper, we introduce a new paradigm to classify MRI using clustering analysis and deterministic relaxation for neural network to improve the classification results.

• Smart Structures and Systems
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• v.24 no.5
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• pp.587-595
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• 2019

Deformation measurement of large scale structures, such as the ground beds of high-rise buildings, tunnels, bridge, and railways, are important for insuring service quality and safety. The pose-relay videometrics method and displacement-relay videometrics method have already presented to measure the pose of non-intervisible objects and vertical subsidence of unstable areas, respectively. Both methods combine the cameras and cooperative markers to form the camera series networks. Based on these two networks, we propose two novel videometrics methods with closed-loop camera series network for deformation measurement of large scale structures. The closed-loop camera series network offers "closed-loop constraints" for the camera series network: the deformation of the reference points observed by different measurement stations is identical. The closed-loop constraints improve the measurement accuracy using camera series network. Furthermore, multiple closed-loops and the flexible combination of camera series network are introduced to facilitate more complex deformation measurement tasks. Simulated results show that the closed-loop constraints can enhance the measurement accuracy of camera series network effectively.

• ETRI Journal
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• v.35 no.5
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• pp.775-785
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• 2013

Because of their exclusive features, millimeter wave directive mesh networks can be considered for small cell backhaul support in urban environments. For this purpose, a network of closely spaced stations has been considered with very directive line-of-sight links operating in the 60-GHz band. An attempt is made to evaluate channel response and interference behavior in such a network, taking into account the effect of building blockage. A simple grid of building blocks is considered as the propagation environment, and wave propagation is simulated using 2.5-dimensional (2.5D) ray tracing (2D with ground effect) to calculate the received signal at different nodes in the network. The results are compared with free space predictions and used to evaluate interference at all nodes in the channel and describe certain characteristics of links, such as the delay profile and the correlation length.