Use of optimal wavelet packet decomposition for the long-term prediction of variable-bit-rate video traffic

Zhang, S.; Chen, J.; She, Liu; Bao, Xulei

doi:10.1049/iet-com.2009.0405

Cited by 8 publications

(7 citation statements)

References 22 publications

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“…Singular signal would distort after traditional filtering. However, wavelet transform is more suitable for singular signal [9–13]. In this paper, wavelet packet decomposition is developed based on wavelet transform.…”

Section: Application Of Wavelet Packet Decomposition In Wind Hybridmentioning

confidence: 99%

“…Wind power is decomposed into low‐frequency and high‐frequency components by wavelet packet to analyse the signal accurately. First floor of the wavelet packet algorithm [10] is as follows:

({, \begin{matrix} Y_{1, 0}^{n} false(t false) = \sum_{k} a_{k - 2 l} Y_{0} false(t false) \\ Y_{1, 1}^{n} false(t false) = \sum_{k} b_{k - 2 l} Y_{0} (t) \end{matrix})

where

Y_{0} false(t false)

is the original signal,

a_{k - 2 l}

and

b_{k - 2 l}

are low‐pass and high‐pass filter coefficients.

Y_{_{1, 0}}^{n} false(t false)

is the low‐frequency coefficient of the decomposition first floor.…”

Section: Application Of Wavelet Packet Decomposition In Wind Hybridmentioning

confidence: 99%

“…

Y_{_{1, 1}}^{n} false(t false)

is the high‐frequency coefficient. Signals after wavelet packet decomposition are reconstructed into low‐frequency and high‐frequency parts as follows [10, 11]:

({, \begin{matrix} Y_{1, 0} false(t false) = \sum_{k} [h_{l - 2 k} Y_{1, 0}^{2 n} (t) + g_{l - 2 k} Y_{1, 0}^{2 n + 1} (t)] \\ Y_{1, 1} false(t false) = \sum_{k} [h_{l - 2 k} Y_{1, 1}^{2 n} (t) + g_{l - 2 k} Y_{1, 1}^{2 n + 1} (t)] \end{matrix})

where

Y_{1, 0} false(t false)

and

Y_{1, 1} false(t false)

are low‐frequency and high‐frequency signals after the first floor of wavelet packet decomposition.

h_{l - 2 k}

and

g_{l - 2 k}

are the coefficients of low‐pass and high‐pass filters.…”

Section: Application Of Wavelet Packet Decomposition In Wind Hybridmentioning

confidence: 99%

See 2 more Smart Citations

Optimal control strategy for a wind power hybrid energy storage system considering output power amendment

Xu³

et al. 2019

J. eng.

View full text Add to dashboard Cite

Section: Application Of Wavelet Packet Decomposition In Wind Hybridmentioning

confidence: 99%

“…Wind power is decomposed into low‐frequency and high‐frequency components by wavelet packet to analyse the signal accurately. First floor of the wavelet packet algorithm [10] is as follows:

({, \begin{matrix} Y_{1, 0}^{n} false(t false) = \sum_{k} a_{k - 2 l} Y_{0} false(t false) \\ Y_{1, 1}^{n} false(t false) = \sum_{k} b_{k - 2 l} Y_{0} (t) \end{matrix})

where

Y_{0} false(t false)

is the original signal,

a_{k - 2 l}

and

b_{k - 2 l}

are low‐pass and high‐pass filter coefficients.

Y_{_{1, 0}}^{n} false(t false)

is the low‐frequency coefficient of the decomposition first floor.…”

Section: Application Of Wavelet Packet Decomposition In Wind Hybridmentioning

confidence: 99%

“…

Y_{_{1, 1}}^{n} false(t false)

is the high‐frequency coefficient. Signals after wavelet packet decomposition are reconstructed into low‐frequency and high‐frequency parts as follows [10, 11]:

({, \begin{matrix} Y_{1, 0} false(t false) = \sum_{k} [h_{l - 2 k} Y_{1, 0}^{2 n} (t) + g_{l - 2 k} Y_{1, 0}^{2 n + 1} (t)] \\ Y_{1, 1} false(t false) = \sum_{k} [h_{l - 2 k} Y_{1, 1}^{2 n} (t) + g_{l - 2 k} Y_{1, 1}^{2 n + 1} (t)] \end{matrix})

where

Y_{1, 0} false(t false)

and

Y_{1, 1} false(t false)

are low‐frequency and high‐frequency signals after the first floor of wavelet packet decomposition.

h_{l - 2 k}

and

g_{l - 2 k}

are the coefficients of low‐pass and high‐pass filters.…”

Section: Application Of Wavelet Packet Decomposition In Wind Hybridmentioning

confidence: 99%

See 1 more Smart Citation

Optimal control strategy for a wind power hybrid energy storage system considering output power amendment

Xu³

et al. 2019

J. eng.

View full text Add to dashboard Cite

“…Moreover, it was shown that the wavelet domain LMK (WLMK) [34] or wavelet domain NLMS [35] have faster convergence and lower prediction error than the time-domain LMK or time-domain NLMS.…”

Section: Research Articlementioning

confidence: 99%

Variable bit rate video traffic prediction based on kernel least mean square method

Haghighat

Kalbkhani

Shayesteh

et al. 2015

IET Image Processing

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“…Apart from the combination with ANN models, some studies implemented wavelet decomposition with the SVM. This approach was used for optimal wavelet packet decomposition combined with the SVM to predict variable bit rate video traffic [27]. The results showed that the proposed model can predict hundreds of consecutive video frame values and it has increased the prediction precision when compared to the SVM alone.…”

Section: Introductionmentioning

confidence: 99%

A Promising Wavelet Decomposition –NNARX Model to Predict Flood

Anuar

Rahman

Soh

et al. 2020

Int. j. electr. comput. eng. syst. (Online)

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Flood is a major disaster that happens around the world. It has caused many casualties and massive destruction of property. Estimating the chance of a flood occurring depends on several factors, such as rainfall, the structure and the flow rate of the river. This research used the neural network autoregressive exogenous input (NNARX) model to predict floods. One of the research challenges was to develop accurate models and improve the forecasting model. This research aimed to improve the performance of the neural network model for flood prediction. A new technique was proposed for modelling nonlinear data of flood forecasting using the wavelet decomposition-NNARX approach. This paper discusses the process of identifying the parameters involved to make a forecast as the rainfall value requires the flow rate of the river and its water level. The original data were processed by wavelet decomposition and filtered to generate a new set of data for the NNARX prediction model where the process can be compared. This research compared the performance of the wavelet and the non-wavelet NNARX model. Experimental results showed that the proposed approach had better performance testing results in relation to its counterpart in terms of hourly forecast, with the mean square error (MSE) of 2.0491e-4 m2 compared to 6.1642e-4 m2, respectively. The proposed approach was also studied for long-term forecast up to 5 years, where the obtained MSE was higher, i.e., 0.0016 m2.

show abstract

Use of optimal wavelet packet decomposition for the long-term prediction of variable-bit-rate video traffic

Cited by 8 publications

References 22 publications

Optimal control strategy for a wind power hybrid energy storage system considering output power amendment

Optimal control strategy for a wind power hybrid energy storage system considering output power amendment

Variable bit rate video traffic prediction based on kernel least mean square method

A Promising Wavelet Decomposition –NNARX Model to Predict Flood

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