• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.221-224
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• 2001

In this paper, we propose the initial optimized structure of the Radial Basis Function Network which is more simple in the part on the structure and converges more faster than Neural Network with the analysis method using Time-Frequency Localization and genetic algorithm. When we construct the hidden node with the Radial Basis Function whose localization is similar with an approximation target function in the plane of the Time and Frequency, we have initial structure of RBFN, After that, we evaluate the parameters of RBF in the network and the parameters needed for the network is more a few. Finally, we make a good decision of the initial structure having an ability of approximation.

• IEMEK Journal of Embedded Systems and Applications
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• v.5 no.2
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• pp.57-66
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• 2010

In this paper, we propose an enhanced voice recognition algorithm using adaptive MFCC(Mel Frequency Cepstral Coefficients) and neural network. Though it is very important to extract voice data from the raw data to enhance the voice recognition ratio, conventional algorithms are subject to deteriorating voice data when they eliminate noise within special frequency band. Differently from the conventional MFCC, the proposed algorithm imposed bigger weights to some specified frequency regions and unoverlapped filterbank to enhance the recognition ratio without deteriorating voice data. In simulation results, the proposed algorithm shows better performance comparing with MFCC since it is robust to variation of the environment.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.649-657
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• 2013

DTN flooding based routing protocol replicate the message copy to increase the delivery like hood that overloads the network resources. The probabilistic routing protocols reduce replication cost by forwarding the message to a node that holds high predictability value to meet its destination. However, the network traffic converges to high probable nodes and produce congestion that triggers the drop of previously stored messages. In this paper, we have proposed a routing protocol called as Connection frequency Buffer Aware Routing Protocol (CFBARP) that uses an adaptive method to maintain the information about the available buffer space at the receiver before message transmission. Furthermore, a frequency based method has been employed to determine the connection recurrence among nodes. The proposed strategy has performed well in terms of reducing message drop, message relay while increases the delivery probability.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.11
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• pp.2281-2285
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• 2009

Powerline communication is the data signal which is modulated by carrier frequency through the installed powerline at in-home or office is transmitted and received signals are separated into data signal with using band-pass filter which cent-frequency is carrier frequency. The home gateway, an equipment which works as an gateway for ubiquitous home network, relays all functions of a home network. The home gateway must always be connected in order to provide seamless services. However it gives unfavorable power consumption. Therefore the needs for working in maximum power saving mode while there is no data traffic and for invoking to the normal function when it is necessary. So, in this paper we survey the development of low power PLC modem monitoring of power consumption and breaking abnormal power in the home Network.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.3C
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• pp.251-258
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• 2012

In this paper, we propose a novel method of multi-node frequency synchronization for distributed networks. The proposed method synchronizes carrier frequencies of all nodes in the network and this enables new entry node to synchronize immediately. Moreover, when several groups exist in the network, inter-group synchronization method is proposed. The proposed distributed frequency synchronization method is expected to be very useful for the military operation scenario that new node entry is in a state of flux and group merging and splitting frequently happen.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.51 no.3
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• pp.321-327
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• 2018

To classify seafloor sediments using a probabilistic neural network (PNN), the frequency-dependent characteristics of broadband acoustic scattering, which make it possible to qualitatively categorize seabed type, were collected from three different geographical areas in Korea. The echo data samples from three types of seafloor sediment were measured using a chirp sonar system operating over a frequency range of 20-220 kHz. The spectrum amplitudes for frequency responses of 35-75 kHz were fed into the PNN as input feature parameters. The PNN algorithm could successfully identify three seabed types: mud, mud/shell and concrete sediments. The percentage probabilities of the three seabed types being correctly classified were 86% for mud, 66% for mud/shell and 72% for concrete sediment.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.170-177
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• 2014

This paper presents a new method of classification of the induction machine faults using Time Frequency Representation, Particle Swarm Optimization and artificial neural network. The essence of the feature extraction is to project from faulty machine to a low size signal time-frequency representation (TFR), which is deliberately designed for maximizing the separability between classes, a distinct TFR is designed for each class. The feature vectors size is optimized using Particle Swarm Optimization method (PSO). The classifier is designed using an artificial neural network. This method allows an accurate classification independently of load level. The introduction of the PSO in the classification procedure has given good results using the reduced size of the feature vectors obtained by the optimization process. These results are validated on a 5.5-kW induction motor test bench.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.137-144
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• 1998

This paper proposed a optimal scale factors technique of a fuzzy-neural network for a load frequency control of two areas power system. The optimal scale factors control technique is optimize from an initial fuzzy logic control rule, and then is learned with an error back propagation learning algorithm of the fuzzy-neural network. In application two areas the load frequency control of the power system, it hopes to have response characteristic better than optimal control technique which is the conventional control technique and to show to minimize a frequency deviation and reaching and settling time of a tie line power flow deviation

• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1375-1384
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• 2014

Flashover of power transmission line insulators is a major threat to the reliable operation of power system. This paper deals with the flashover prediction of polymeric insulators used in power transmission line applications using the novel condition monitoring technique developed by PD signal time-frequency map and neural network technique. Laboratory experiments on polymeric insulators were carried out as per IEC 60507 under AC voltage, at different humidity and contamination levels using NaCl as a contaminant. Partial discharge signals were acquired using advanced ultra wide band detection system. Salient features from the Time-Frequency map and PRPD pattern at different pollution levels were extracted. The flashover prediction of polymeric insulators was automated using artificial neural network (ANN) with back propagation algorithm (BPA). From the results, it can be speculated that PD signal feature extraction along with back propagation classification is a well suited technique to predict flashover of polymeric insulators.

• Health Policy and Management
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• v.29 no.2
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• pp.138-145
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• 2019

Background: The purpose of this study is supposed to figure out the keyword network from 2009 to 2018 with social network analysis and provide the research data that can help the Korea government's policy making on smoking cessation. Methods: First, frequency analysis on the keyword was performed. After, in this study, I applied three classic centrality measures (degree centrality, betweenness centrality, and eigenvector centrality) with R 3.5.1. Moreover, I visualized the results as the word cloud and keyword network. Results: As a result of network analysis, 'smoking' and 'smoking cessation' were key words with high frequency, high degree centrality, and betweenness centrality. As a result of looking at trends in keyword, many study had been done on the keyword 'secondhand smoke' and 'adolescent' from 2009 to 2013, and 'cigarette graphic warning' and 'electronic cigarette' from 2014 to 2018. Conclusion: This study contributes to understand trends on smoking cessation study and seek further study with the keyword network analysis.