• Proceedings of the Korean Nuclear Society Conference
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• 1996.11a
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• pp.67-72
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• 1996

Wavelets are localized in space and in frequency. This localization properties result from the multiresolution analysis of wavelets. The wavelet transform can be used to detect singularity of dynamic systems after the signal is de-noised. We applied the wavelet transform decomposition and do-noising procedures to the Hanaro dynamics consisting of 39 nonlinear differential equation plus Gaussian noise. The numerical tests demonstrate that the wavelet transform de-noising is effective for detection of the abrupt reactivity change and computationally efficient. Thus this wavelet theory could be profitably utilized in a real-time system for automatic event recognition (e.g., reactor condition monitoring).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.431-434
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• 2003

As a wavelet transform which is presented as a new technique of signal processing field has time and frequency localization capabilities, it's possible for multiresolution analysis as well as easy to analyze various signal. So it is being applied in many fields recently. And when two wavelet base were designed to form Hilbert transform pair, wavelet pair show superior performance than the existing DWT(discrete wavelet transform) in data detection of pulse type. Therefore in this paper, we detected position of impulse noise by using two dyadic wavelet base which is designed by truncated coefficient vector.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.8
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• pp.1120-1124
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• 2013

Our objective of this study was to reduce radio frequency coil (RF) control time at 3 T MRI systems. A compressed method is proposed with a convex optimization and pseudo-inverse method in multi-channel RF coils. After applying the proposed methods, fields are homogenized with less field data. Even with 80% compression, the fields are well homogenized and localized, indicating that mapping requires only 20% of the original data. Detailed values are compared between each compressed result in and outside the region of interest at 3 T.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.301-303
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• 2000

This paper presents Kohonen neural network and wavelet transform analysis based technique for industrial peak load forecasting for the purpose of peak demand control. Firstly, one year of historical load data were sorted and clustered into several groups using Kohonen neural network and then wavelet transforms are adopted using the Biorthogonal mother wavelet in order to forecast the peak load of one hour ahead. The 5-level decomposition of the daily industrial load curve is implemented to consider the weather sensitive component of loads effectively. The wavelet coefficients associated with certain frequency and time localization is adjusted using the conventional multiple regression method and the components are reconstructed to predict the final loads through a six-scale synthesis technique.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1464-1467
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• 2005

In this research, we developed a system, which can find the location of vehicle when people park their cars in a big parking lot or large area. People can find their cars readily through this simple device and they can save their time and effort. This is the purpose of this research. Performing this, detection of electromagnetic wave's direction is needed and we used shielding effectiveness of electromagnetic waves for the method of it. An absolute coordinate indicates four directions (E, W, S, N) by using an electronic compass module, and it is needed for the localization. The device can check the received count of the electromagnetic waves coming from all other directions through the system, which is installed in the vehicle. The direction recorded the least received count would be the location of the parked vehicles. We can add on the function of this research by using the same frequency of cars alarm goods. Also, it is useful in the huge indoor parking lot.

• Journal of Ocean Engineering and Technology
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• v.16 no.5
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• pp.61-65
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• 2002

The fault detection and diagnosis of rotating machinery widely used in plants including the ship are important for maintaining the performance of Plants. Recently, the wavelet transform has been recognized an efficient method to detect a little variation of physical quantities by the synchronous localization of time and frequency domains using the translation and dilation of signals. In this Paper, In order to develop efficient and reliable fault detection and diagnosis system rotating machines, the performance of wavelet transformation to detect a little variation of machine status and neural network to diagnose the cause of machine faults are investigated and experimented.

• ETRI Journal
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• v.29 no.4
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• pp.430-436
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• 2007

An OFDM/offset QAM (OQAM)-IOTA system uses the isotropic orthogonal transform algorithm (IOTA) function, which has good localization properties in the time and frequency domains. This is employed instead of the guard interval used in a conventional OFDM/QAM system in order to be robust for multi-path channels. However, the conventional channel estimation scheme is not valid for an OFDM/OQAM-IOTA system due to the intrinsic inter-symbol interference of the IOTA function. In this paper, a condition is derived to reduce the intrinsic interference of the IOTA function. This condition is obtained with the proposed pilot structure used for perfect channel estimation. We also derive the preamble structure appropriate for practical channel estimation of the OFDM/OQAM-IOTA system. Simulation results show that the OFDM/OQAM-IOTA system with the proposed preamble structure performs better than the conventional OFDM system, and it has the additional advantage of an increased data transmission rate which corresponds to the guard interval retrieval.

• Journal of IKEEE
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• v.4 no.2 s.7
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• pp.233-240
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• 2000

This paper presents fuzzy clustering and wavelet transform analysis based technique for the industrial hourly load forecasting fur the purpose of peak demand control. Firstly, one year of historical load data were sorted and clustered into several groups using fuzzy clustering and then wavelet transform is adopted using the Biorthogonal mother wavelet in order to forecast the peak load of one hour ahead. The 5-level decomposition of the daily industrial load curve is implemented to consider the weather sensitive component of loads effectively. The wavelet coefficients associated with certain frequency and time localization is adjusted using the conventional multiple regression method and the components are reconstructed to predict the final loads through a five-scale synthesis technique. The outcome of the study clearly indicates that the proposed composite model of fuzzy clustering and wavelet transform approach can be used as an attractive and effective means for the industrial hourly peak load forecasting.

• Proceedings of the KIEE Conference
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• 1999.11b
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• pp.242-244
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• 1999

This paper proposes a novel wavelet transform based algorithm for the seasonal load forecasting. In this paper, Daubechies DB2, DB4 and DB10 wavelet transforms are adopted to predict the seasonal loads and the numerical results reveal that certain wavelet components can effectively be used to identify the load characteristics in electric power systems. The wavelet coefficients associated with certain frequency and time localization are adjusted using the conventional multiple regression method and then reconstructed. In order to forecast the final loads through a four-scale synthesis technique. The outcome of the study clearly indicates that the wavelet transform approach can be used as an attractive and effective means of the seasonal load forecasting.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.439-442
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• 1997

In our research, we will extract diagnostic parameters by LPC method and wavelet transform. Then, we will design artificial neural network which is based on RBF that can express input features in terms of fuzzy. Because PVC(Premature Ventricular Contraction) has possibility to cause heart attack, the detection of PVC is a very significant problem. To deal with this problem, LPC method which gives different coefficients or different morphologies and wavelet transform which has superior localization nature of time-frequency, are used to extract effective parameters or classification of normal and PVC. Because RBF network can allocate an input feature to the membership degree of each category, total system will be more flexible.