By combining wavelet transform (WT) with neural network theory, a novel approach is put forward to detect transient
fault and analyze voltage stability. The application of signal denoising based on the statistic rule is proposed to determine
the threshold of each order of wavelet space. In a view of the inter relationship of wavelet transform and neural network,
the whole and local fractal exponents obtained from WT coefficients as features are presented for extracting signal
features. The effectiveness of the new algorithm used to extract the characteristic signal is described, which can be
realized by the value of those types of transient signal. This model incorporates the advantages of morphological filter
and multi-scale WT to extract the feature of fault signal meanwhile restraining various noises. Besides, it can be
implemented in real time using the available hardware. The effectiveness of this model was verified with the voltage
stability analysis of simulation results.
An effective approach for fault diagnosis of aeroengine based on integration of wavelet analysis and neural networks is
presented. The wavelet transform can accurately localizes the characteristics of a signal in time-frequency domains and
in a view of the inter relationship of wavelet transform between exponent theory, the whole and local exponents obtained
from wavelet transform coefficients as features are presented for extracting fault signals, which are inputted into radial
basis function for fault pattern recognition. The fault diagnosis model of aero-engine is established and the improved
Levenberg-Marquardt training algorithm is used to fulfill the network structure and parameter identification. By
choosing enough samples to train the fault diagnosis network and the information representing the faults input into the
neural network, the fault pattern can be determined. The robustness of wavelet neural network for fault diagnosis is
discussed. The practical fault diagnosis for aeroengine vibration approves to be accurate and comprehensive.
To overcome the problem that the video information packet lost occasionally when the video stream transfers in the low dependable
channel, it is absolutely necessary to adopt effective error control technique to recover the false video signal.
According to the multiple-reference frames characteristic of H.264/AVC, this paper improves the traditional boundary
matching algorithm and proposes a multiple-reference frames enhanced weighted boundary matching error concealment
algorithm. The simulation experiments show that the proposed algorithm improves the limitations of the traditional
temporal error concealment and decreases the matching distortion contrasting normal single-reference frame boundary matching
algorithm at the condition of strong movement and video scenes switching. Therefore more exact motion
vector would be obtained to restrain the diffusion of video error effectively. Moreover, the improved algorithm run on
decoder end, without any changes of H.264 video stream structure and extra burden in transport channel, and has wide
adaptability in multiple media transmission.
To improve the low-speed dynamic performance of induction motor in direct torque control (DTC), a novel method of
stator resistance identification based on wavelet network (WN) is presented and the determination of wavelet network
structure is discussed. The inputs of the WN are the current error and the change in the current error and the output of the
WN is the stator resistance error. The improved least squares algorithm (LSA) is used to fulfill the network structure and
parameter identification. By the use of wavelet transform that accurately localizes the characteristics of a signal both in
the time and frequency domains, the occurring instants of the stator resistance change can be identified by the multi-scale
representation of the signal. Once the instants are detected, the accurate stator flux vector and electromagnetic torque are
acquired by the parameter estimator, which makes the DTC applicable in the low region, optimizing the inverter control
strategy. By detailed comparison between the wavelet and the typical backward-propagation (BP) neural network, the
simulation results show that the proposed method can efficiently reduce the torque ripple and current ripple, superior to
the BP neural network.
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