• The Journal of the Acoustical Society of Korea
• /
• v.20 no.5
• /
• pp.21-27
• /
• 2001

Impulsive sound and vibration signals in gear system are often associated with their faults. Thus these impulsive sound and vibration signals can be used as indicators in condition monitoring of gear system. The traditional continuous wavelet transform has been used for detection of impulsive signals. However, it is often difficult for the continuous wavelet transform to identify spikes at high frequency and meshing frequencies at low frequency simultaneously since the continuous wavelet transform is to apply the linear scaling (a-dilation) to the mother wavelet. In this paper, the spike wavelet transform is developed to extract these impulsive sound and vibration signals. Since the spike wavelet transform is to apply the non-linear scaling, it has better time resolution at high frequency and frequency resolution at low frequency than that of the continuous wavelet transform respectively. The spike wavelet transform can be, therefore, used to detect fault position clearly without the loss of information for the damage of a gear system. The spike wavelet transform is successfully is applied to detection of the gear fault with tip breakage.

• Journal of Advanced Navigation Technology
• /
• v.20 no.4
• /
• pp.298-304
• /
• 2016

In this paper, a wavelet packet transform method is proposed for improving the altitude control performance of quadrotor UAV using an ultrasonic rangefinder. A ground tests are conducted using an ultrasonic rangefinder that is much used for vertical takeoff and landing. An ultrasonic rangefinder suffers from signal's spike due to specular reflectance and acoustic noise. The occurred spikes in short time span need to be analyzed at both sides time and frequency domain. The analyzed spikes of the ultrasonic rangefinder using a wavelet packet transform. Compared with the discrete wavelet transform, the wavelet packet decomposition can obtain more abundant time-frequency localization information, so it is more suitable for analyzing and processing ultrasonic signals spike. Experimental results show that it can effectively remove the spikes of the ultrasonic rangefinder.

• Journal of Advanced Navigation Technology
• /
• v.20 no.3
• /
• pp.196-202
• /
• 2016

In this paper, a stationary wavelet transform method is proposed for improving the altitude control performance of quadrotor UAV using an ultrasonic rangefinder. A ground tests are conducted using an ultrasonic rangefinder that is much used for vertical takeoff and landing. An ultrasonic rangefinder suffers from signal's spike due to specular reflectance and acoustic noise. The occurred spikes in short time span need to be analyzed at both sides time and frequency domain. It was known that stationary wavelet transform is the transferring solution to the problem occurred by down sampling from DWT also more efficient to remove noise than DWT. The analyzed spikes of the ultrasonic rangefinder using a stationary wavelet transform and experimental results show that it can effectively remove the spikes of the ultrasonic rangefinder.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.35S no.2
• /
• pp.70-78
• /
• 1998

Spike detection in long-term EEG monitoring forepilepsy by wavelet transform(WT), artificial neural network(ANN) and the expert system is presented. First, a small set of wavelet coefficients is used to represent the characteristics of a singlechannel epileptic spikes and normal activities. In this stage, two parameters are also extracted from the relation between EEG activities before the spike event and EEG activities with the spike. then, three-layer feed-forward network employing the error back propagation algorithm is trained and tested using parameters obtained from the first stage. Spikes are identified in individual EEG channels by 16 identical neural networks. Finally, 16-channel expert system based on the context information of adjacent channels is introducedto yield more reliable results and reject artifacts. In this study, epileptic spikes and normal activities are selected from 32 patient's EEG in consensus among experts. The result showed that the WT reduced data input size and the preprocessed ANN had more accuracy than that of ANN with the same input size of raw data. Ina clinical test, our expert rule system was capable of rejecting artifacts commonly found in EEG recodings.

• Proceedings of the KOSOMBE Conference
• /
• v.1997 no.05
• /
• pp.358-361
• /
• 1997

This paper describes a multichannel epileptic seizure detection algorithm based on wavelet transform(WT), artificial neural network(ANN) and expert system. First, through the WT, a small number of wavelet coefficients is used to represent the single channel epileptic spike. Next, 3-layer feed-forward network employing the error back propagation algorithm is trained and tested using parameters obtained above. Finally, 16 channel expert system which is based on clinical experience is introduced as a artifact rejection and reliable detection. The suggested algorithm was implemented on personal computer(PC). Two main events i.e., epileptiform and normal activities, were selected from 32 person's EEGs(normal: 20, seizure disorder: 12) in consensus among experts. The result was that WT reduced data input size and ANN detected 97 of the 100 EEGs containing definite spike - sensitivity of 97%. Expert rule system was capable of rejecting a wide variety of artifacts commonly found in EEG recordings. It also reduced false positive detections of ANN.

• Proceedings of the KIEE Conference
• /
• 2003.07d
• /
• pp.2804-2806
• /
• 2003

생체신호는 생리학이나 해부학에서 주로 다루어졌으나, 최근 컴퓨터 시스템의 발전으로 공학적인 접근이 활발히 진행되고 있다. 특히 뇌의 정보를 보여주는 EEG(Electroencephalogram) 신호의 각 주파수 대역 별 에너지 분석은 의학분야에서도 매우 큰 비중을 두고 있다. 특정 뇌신경 관련질환이 갖는 대역별 주파수 특징과 Spike등을 분석하는 것은 치료와 예방에 아주 좋은 방법의 하나가 될 수 있다. 본 논문에서는 신호처리에서 높은 효율을 보이는 Wavelet Transform을 이용하여 알츠하이머병의 EEG 신호를 분석하였다.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.2
• /
• pp.174-184
• /
• 2013

For the successful R&D and deployment of LED-based green energy lighting systems, the real-time power quality measurement of both various non-linear power signals including pulse waveform, spike waveform, etc and the undesired-signals including harmonics, sag, swell, etc is required. In this paper, we propose a low-cost power quality measurement (PQM) method for low- (60Hz-several KHz) to high-frequency (several tens KHz) power signals, which are generated by green-energy lighting systems, and implement a PQM testbed using TI TMS320F28335 MCU. The proposed algorithm is programmed using C in the CCS (Code Composer Studio) 3.3 environment and is verified using test signals generated by an arbitrary signal generator, NF-WF1974. In the implemented testbed, we can measure various non-linear current signals that LED SMPS generates, analyze harmonics by fast Fourier transform, and test sag, swell, and interruption using wavelet transform.