• International Journal of Ocean Engineering and Technology Speciallssue:Selected Papers
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• v.6 no.1
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• pp.1-6
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• 2003

This study presents the results of series of studies, which are mainly devoted to the application of wavelet transforms to various problems in ocean engineering. Both continuous and discrete wavelet transforms were used. These studies attempted to solve detection of wave directionality, detection of wave profile, and decoupling of the rolling component from free roll decay tests. The results of these analysis, using wavelet transform, demonstrated that the wavelet transform can be a useful tool in analyzing many problems in the filed of ocean engineering.

• 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.

• Journal of Ocean Engineering and Technology
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• v.17 no.3 s.52
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• pp.1-6
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• 2003

This study presents the results of series of studies, which are mainly devoted to the application of wavelet transforms to various problems in ocean engineering. Both continuous and discrete wavelet transforms were used. These studies attempted to solve detection of wave directionality, detection of wave profile, and decoupling of the rolling component from free roll decay tests. The results of these analysis, using wavelet transform, demonstrated that the wavelet transform can be a useful tool in analyzing many problems in the filed of ocean engineering.

• Journal of Korea Multimedia Society
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• v.9 no.3
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• pp.287-295
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• 2006

This paper compares two different approaches based on wavelet and Gabor decomposition towards representing the texture of an arbitrary region. The Gabor-domain mean and standard deviation combination is considered to be best in representing the texture of rectangular regions. However, texture representation of arbitrary regions would enable generalized object-based image retrieval and other applications in the future. In this study, we have found that the wavelet features perform better than the Gabor features in representing the texture of arbitrary regions. Particularly, the wavelet-domain standard deviation and entropy combination results in the best retrieval accuracy. Based on our experiment with texture image sets, we present and compare tile retrieval accuracy of multiple wavelet and Gabor feature combinations.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.2
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• pp.98-103
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• 2008

There are the frequency shift keying(FSK), phase shift keying(PSK) and amplitude shift keying(ASK) in the conventional digital communications method. In this paper, We proposed a new robust wavelet shift keying system using scaling and wavelet function in the digital communication. Wavelet Transform consist of a low frequency and high frequency coefficient. When the input signal is one, if it finds the impulse response, the signal is separated from the scaling and wavelet function. The binary data is encoded by modulator which assigned the scaling function to 1(one), and wavelet to zero(0). It was demonstrated by experiment that the proposed algorithm can be a robust noise.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.668-676
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• 2017

Recently, the importance of big data has been emphasized with the development of smartphone, web/SNS. As a result, MapReduce, which can efficiently process big data, is receiving worldwide attention because of its excellent scalability and stability. Since big data has a large amount, fast creation speed, and various properties, it is more efficient to process big data summary information than big data itself. Wavelet histogram, which is a typical data summary information generation technique, can generate optimal data summary information that does not cause loss of information of original data. Therefore, a system applying a wavelet histogram generation technique based on MapReduce has been actively studied. However, existing research has a disadvantage in that the generation speed is slow because the wavelet histogram is generated through one or more MapReduce Jobs. And there is a high possibility that the error of the data restored by the wavelet histogram becomes large. However, since the wavelet histogram generation system based on the MapReduce developed in this paper generates the wavelet histogram through one MapReduce Job, the generation speed can be greatly increased. In addition, since the wavelet histogram is generated by adjusting the error boundary specified by the user, the error of the restored data can be adjusted from the wavelet histogram. Finally, we verified the efficiency of the wavelet histogram generation system developed in this paper through performance evaluation.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.3
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• pp.184-191
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• 2018

Orthogonal frequency division multiplexing(OFDM) is a multicarrier modulation(MCM) system that enables high-speed communications using multiple carriers and has advantages of power and spectral efficiency. Therefore, this study aims to complement the existing shortcomings and to design an efficient MCM system. The proposed system uses the inverse discrete wavelet transform(IDWT) operation instead of the inverse fast Fourier transform(IFFT) operation. The bit error rate(BER), spectral efficiency, and peak-to-average power ratio(PAPR) performance were compared with the conventional OFDM system through the OFDM system design based on wavelet transform. Our results showed that the conventional OFDM and Wavelet-OFDM exhibited the same BER performance, and that the Wavelet-OFDM using the discrete Meyer wavelet had the same spectral efficiency as the conventional OFDM. In addition, all systems of Wavelet-OFDM based on various wavelets confirm a PAPR performance lower than that of conventional OFDM.

• Spatial Information Research
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• v.17 no.4
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• pp.455-462
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• 2009

A method to find the nearly optimal PSNR values for compression was tried to remotely sensed images. There is no rule to find the best wavelet pairs for image processing. The expected wavelet pairs following the suggested algorithm showed the optimal result for various kinds of images. Firstly, the PSNR variations with three wavelet families were analyzed. In many cases the longer wavelet filter shows the higher PSNR value, but the rate is getting less in orthogonal wavelet families. Wavelets with moderate filter length are suggested at the point of computational cost. For biorthogonal families it was hard to predict from the length of filters. Multiresolution wavelet analysis was used up to level 3 with three kinds of wavelet families. Biorthogonal wavelet family showed irregular pattern to get the maximum PSNR values, while orthogonal wavelet families showed regular pattern. In orthogonal wavelet families the nearly optimal wavelet pair can be predicted from the level 1.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.635-639
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the modeling of chaotic nonlinear systems. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the modeling performance for chaotic nonlinear systems and compare it with those of the FNN and the WFM.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.111-118
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the solution of the tracking problem for mobile robots. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the tracking performance for mobile robot and compare it with those of the FNN and the WFM.