• ETRI Journal
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• v.32 no.2
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• pp.184-194
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• 2010

Ethernet ring protection (ERP) technology, which is defined in ITU-T Recommendation G.8032, has been developed to provide carrier grade recovery for Ethernet ring networks. However, the filtering database (FDB) flush method adopted in the current ERP standard has the drawback of introducing a large amount of transient traffic overshoot caused by flooded Ethernet frames right after protection switching. This traffic overshooting is especially critical when a ring provides services to a large number of clients. According to our experimental results, the traditional FDB flush requires a link capacity about sixteen times greater than the steady state traffic bandwidth. This paper introduces four flush optimization schemes to resolve this issue and investigates how the proposed schemes deal with the transient traffic overshoot on a multi-ring network under failure conditions. With a network simulator, we evaluate the performance of the proposed schemes and compare them to the conventional FDB flush scheme. Among the proposed methods, the extended FDB advertisement method shows the fastest and most stable protection switching performance.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.11 s.353
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• pp.1-7
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• 2006

This paper consider a code-division multiple-access(CDMA) wireless ad-hoc network in low-SINR regime. In previous research [6], there has been proposed a algorithm for achieving global optimum at high SINR regime, but has not been fully investigated at low-SINR regime. In this paper, we focus on a case where SINR is much smaller than 1, and propose a algorithm that is suitable for low-SINR regime.

• Smart Structures and Systems
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• v.18 no.6
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• pp.1233-1250
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• 2016

Cracks in plate-like structures are some of the main reasons for destruction of the entire structure. In this study, a novel two-stage methodology is proposed for damage detection of flexural plates using an optimized artificial neural network. In the first stage, location of damages in plates is investigated using curvature-moment and curvature-moment derivative concepts. After detecting the damaged areas, the equations for damage severity detection are solved via Bat Algorithm (BA). In the second stage, in order to efficiently reduce the computational cost of model updating during the optimization process of damage severity detection, multiple damage location assurance criterion index based on the frequency change vector of structures are evaluated using properly trained cascade feed-forward neural network (CFNN) as a surrogate model. In order to achieve the most generalized neural network as a surrogate model, its structure is optimized using binary version of BA. To validate this proposed solution method, two examples are presented. The results indicate that after determining the damage location based on curvature-moment derivative concept, the proposed solution method for damage severity detection leads to significant reduction of computational time compared with direct finite element method. Furthermore, integrating BA with the efficient approximation mechanism of finite element model, maintains the acceptable accuracy of damage severity detection.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.178-185
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• 2009

A signal processing algorithm is proposed for end point detection which is used commonly in accelerometers-based pattern recognition problem. In the conventional method, end points are detected by manual manipulation with an additive button or algorithm based on statistical computation and highpass filtering to cause critical time delay and difficulty for parameters optimization. As an solution, we propose a focused Time Lagged Feedforward Network(TLFN) with respect to a differential signal of acceleration, which is widely applied for time series forecasting. The simple experiment is conducted with handwriting and the detection performance and response characteristic of the proposed algorithm is tested and analyzed.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2456-2467
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• 2018

Wireless multimedia sensor networks (WMSNs) require real-time quality-of-service (QoS) guarantees to be provided by the network. The end-to-end delay is very critical metric for QoS guarantees in WMSNs. In WMSNs, due to the transmission errors incurred over wireless channels, it is difficult to obtain reliable delivery of data in conjunction with low end-to-end delay. In order to improve the end-to-end delay performance, the system has to drop few packets during network congestion. In this article, our proposal is based on optimization of end-to end delay for WMSNs. We optimize end-to-end delay constraint by assuming that each packet is allowed fixed number of retransmissions. To optimize the end-to-end delay, first, we compute the performance measures of the system, such as end-to-end delay and reliability for different network topologies (e.g., linear topology, tree topology) and against different choices of system parameters (e.g., data rate, number of nodes, number of retransmissions). Second, we study the impact of the end-to-end delay and packet delivery ratio on indoor and outdoor environments in WMSNs. All scenarios are simulated with multiple run-times by using network simulator-2 (NS-2) and results are evaluated and discussed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.5
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• pp.705-711
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• 2013

In this paper, we introduce an design of multi-output fuzzy neural networks based on Interval Type-2 fuzzy set. The proposed Interval Type-2 fuzzy set-based fuzzy neural networks with multi-output (IT2FS-based FNNm) comprise the network structure generated by dividing the input space individually. The premise part of the fuzzy rules of the network reflects the individuality of the division space for the entire input space and the consequent part of the fuzzy rules expresses three types of polynomial functions with interval sets such as constant, linear, and modified quadratic inference for pattern recognition. The learning of fuzzy neural networks is realized by adjusting connections of the neurons in the consequent part of the fuzzy rules, and it follows a back-propagation algorithm. In addition, in order to optimize the network, the parameters of the network such as apexes of membership functions, uncertainty factor, learning rate and momentum coefficient were automatically optimized by using real-coded genetic algorithm. The proposed model is evaluated with the use of numerical experimentation.

• Korean Management Science Review
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• v.29 no.3
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• pp.81-89
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• 2012

Since introduced by Vining and Myers in 1990, the concept of dual response approach based on response surface methodology has widely been investigated and adopted for the purpose of robust design. Separately estimating mean and variance responses, dual response approach may take advantages of optimization modeling for finding optimum settings of input factors. Explicitly assuming functional relationship between responses and input factors, however, it may not work well enough especially when the behavior of responses are poorly represented. A sufficient number of experimentations are required to improve the precision of estimations. This study proposes an alternative to dual response approach in which additional experiments are not required. An artificial neural network has been applied to model relationships between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Training, validating, and testing a neural network with empirical process data, an artificial data based on the neural network may be generated and used to estimate response functions without performing real experimentations. A drug formulation example from pharmaceutical industry has been investigated to demonstrate the procedures and applicability of the proposed approach.

• Journal of KIISE:Software and Applications
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• v.33 no.4
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• pp.440-448
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• 2006

Self-Organizing Map (SOM) is an unsupervised learning neural network and it is used for preserving the structural relationships in the data without prior knowledge. SOM has been applied in the study of complex problems such as vector quantization, combinatorial optimization, and pattern recognition. This paper proposes a new usage of SOM as a tool for schema transformation hoping to achieve more efficient genetic process. Every offspring is transformed into an isomorphic neural network with more desirable shape for genetic search. This helps genes with strong epistasis to stay close together in the chromosome. Experimental results showed considerable improvement over previous results.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1220-1225
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• 2010

This paper proposes a novel active noise control (ANC) approach that uses an IIR filter and neural network techniques to effectively reduce interior noise. We construct a multiple-channel IIR filter module which is a linearly augmented framework with a generic IIR model to generate a primary control signal. A three-layer perceptron neural network is employed for establishing a secondary-path model to represent air channels among noise fields. Since the IIR module and neural network are connected in series, the output of an IIR filter is transferred forward to the neural model to generate a final ANC signal. A gradient descent optimization based learning algorithm is analytically derived for the optimal selection of the ANC parameter vectors. Moreover, re-estimation of partial parameter vectors in the ANC system is proposed for online learning. Lastly, we present the results of a numerical study to test our ANC methodology with realistic interior noise measurement obtained from Korean railway trains.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.699-702
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• 2000

The automation and control of robotic welding process is a very complex assignment because the system is affected by a number of variables which are very difficult to determine or predict in practice. Not only the optimization of the robotic welding process is considered from the point of view of the time and the cost of manufacturing. as well as quality of the weldment. the human factors of the production and many other factors must taken into consideration. hi order to determine the optimal parameters of robotic welding process, it is necessary to build a computer model representing all parameters influencing the welding process as well as the mutual dependence between them. This paper presents an approach to modeling the robotic welding process in which all parameters affecting the welding process are included using a neural network. A detailed analysis of the simulation results has been carried out to evaluate the proposed neural network model.