• 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.

• 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.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.3
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• pp.173-182
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• 2015

A real-time, Indoor navigation systems utilize ultra-wide band (UWB), radio-frequency identification (RFID) and received signal strength (RSS) techniques that encompass WiFi, FM, mobile communications, and other similar technologies. These systems typically require surplus infrastructure for their implementation, which results in significantly increased costs and complexity. Therefore, as a solution to reduce the level of cost and complexity, an inertial measurement unit (IMU) and quick response (QR) codes are utilized in this paper to facilitate navigation with the assistance of a smartphone. The QR code helps to compensate for errors caused by the pedestrian dead reckoning (PDR) algorithm, thereby providing more accurate localization. The proposed algorithm having IMU in conjunction with QR code shows an accuracy of 0.64 m which is higher than existing indoor navigation techniques.

• 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.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1999.05a
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• pp.169-172
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• 1999

Recently, the wavelet transform has been a new and powerful tool for signal processing. It is more suitable specially for the feature extraction and detection of non-stationary signals than traditional methods such as, the Fourier Transform(FT), the Fast Fourier Transform(FFT) and the Least Square Method etc. because of the characteristic of the multi-scale analysis and time-frequency domain localization. The wavelet transform has been developed for the analysis of PD pulse signal to raise in the progress of insulation degradation. In this paper, the wavelet transform was applied to one foundational method for feature extraction. For the obtain experimental data, a computer-aided partial discharge measurement system with a single acoustic sensor was used. If we are applying to the neural network method the accumulated data through the extracted feature, it is expected that we can detect the PD pulse signal in the insulation materials on the on-line.

• 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).

• 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.

• 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 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.

• 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.