Wavelet Packet Energy-Entropy Feature Extraction and Principal Component Analysis for Signal Classification

Varanis, Marcus; Pederiva, Robson

doi:10.5540/03.2015.003.01.0471

Cited by 9 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After Shannon, other researchers in different fields used the entropy method to analyze the signal and time series. (Chen & Li 2014;Varanis & Pederiva 2015). In this regard, Mishra et al (2009), used the concept of Entropy to study the local and temporal changes of the rainfall time series in the US state of Texas.…”

Section: Introductionmentioning

confidence: 99%

Assessing climate change effects on declining groundwater levels using wavelet entropy (case study of Khorramabad city)

2021

View full text Add to dashboard Cite

Changing global climate predicts a warmer future which may alter the hydrological cycle, surface water as well as groundwater resource. The Entropy wavelet criterion is a new indicator to analyze the time series fluctuations. In this study, the effective factors of decreasing the groundwater level in Khorramabad city during the years 2005–2018 have been evaluated by the use of Entropy wavelet criterion. In general, it can be said that the decreasing of Entropy wavelet criterion or time series complexity of a phenomenon shows the time series decrease of fluctuations natural amounts, which it leads to an unfavorable trend. In this regard, in order to identify the affecting factors of the groundwater level decrease in Khorramabad, the groundwater level has been divided into 4 time periods, and after being investigated, the monthly time series of runoff, temperature, and precipitation of this city were divided into 4 periods. Each of these subset were decomposed into other several subsets at different time scales under the wavelet transform, and finally, after calculation of the normalized wavelet energy for this subset, its Entropy wavelet criterion was calculated for each period. Investigation of Entropy wavelet complexity shows a 21.3% decrease in groundwater level in the second period, but in the third and fourth periods, it has increased by 145 and 272%, respectively. Also, according to the results of Entropy wavelet changes analyzing for the precipitation time series, 35.2, 32.8, and 10.06% decrease in the second, third, and fourth periods were shown. The air temperature time series complexity decreased of 26.8% only in the third time period and in the second and fourth period, it shows an increase of 29.65 and 34.7%, respectively. However, the runoff time series did not show any reduction complexity according to the entropy wavelet criterion. These results indicate that the impact of climatic factors has been more effective than human factors in reducing the groundwater level of Khorramabad.

show abstract

Section: Introductionmentioning

confidence: 99%

Assessing climate change effects on declining groundwater levels using wavelet entropy (case study of Khorramabad city)

2021

View full text Add to dashboard Cite

show abstract

“…Shannon (1948) presented the entropy concept to access additional information about time series. Many researches have been investigated about Shannon entropy concept in order for analyzing signals (Bercher & Vignat 2000;Shardt & Huang 2013;Chen & Li 2014;Castillo et al 2015;Singh & Cui 2015;Varanis & Pederiva 2015).…”

Section: Introductionmentioning

confidence: 99%

Recognizing relationship between groundwater level and hydrological time series (Case study: Ardabil plain)

Vousoughi

2021

Preprint

View full text Add to dashboard Cite

Two approaches to identify the relation between hydrological time series (rainfall and runoff) and groundwater level (GWL) were used in the Ardabil plain. In this way, Wavelet-entropy measure (WEM) and wavelet transform coherence (WTC) as two approaches of wavelet transform (WT) were used. WEM have been considered as a criterion for the degree of time series fluctuations and WTC present common time-frequency space. In WEM calculation, monthly rainfall, runoff and GWL time series were divided into three different time periods and decomposed to multiple frequent time series and then, the energies of wavelet were calculated for each sub-series. The result showed WEM reduction in rainfall, runoff and GWL. The reduction of WEM presents the natural fluctuations decrease of time series. The reduction of entropy for runoff, rainfall and GWL time series were about 1.58, 1.36 and 29% respectively, it is concluded that fluctuation reduction of hydrological time series has relatively not more effect on the oscillation patterns of GWL signal. In this regard, it could be concluded that the human activities such as water driving from wells can be played main role in the reduction of GWL in Ardabil plain. WTC findings showed that runoff had most coherence (0.9-1) among the hydrological variables with GWL time series in the frequency bands of 4-8 and 8-16 months.

show abstract

“…A 32-characteristic vector is obtained with 16 relative energy parameters and 16 entropy parameters. The dimension of the characteristic vector of each signal is reduced by separately using the Principal Components Analysis (PCA) method [4,10]. The dimension reduction is necessary, because in these types of application the use of only Energy and Entropy parameters showed ineffective for the classification [4].…”

Section: Model For Classificationmentioning

confidence: 99%

The influence of the wavelet filter in the parameters extraction for signal classification: An experimental study.

Varanis¹,

Pederiva²

2017

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

Self Cite

View full text Add to dashboard Cite

Abstract. This paper treats the influence of the wavelet filters on the parameter extraction for signal classification. Therefore, a database with vibration signal failures of an induction motor in stationary case was utilized. The classification is done by techniques based on the wavelet packet transform and dimension reduction, using the principal component analysis (PCA). Different wavelet filters with different supports are used for comparison. The use of signals from a validated experimental bench shows that the results of the classification model has a high precision.

show abstract

Wavelet Packet Energy-Entropy Feature Extraction and Principal Component Analysis for Signal Classification

Cited by 9 publications

References 9 publications

Assessing climate change effects on declining groundwater levels using wavelet entropy (case study of Khorramabad city)

Assessing climate change effects on declining groundwater levels using wavelet entropy (case study of Khorramabad city)

Recognizing relationship between groundwater level and hydrological time series (Case study: Ardabil plain)

The influence of the wavelet filter in the parameters extraction for signal classification: An experimental study.

Contact Info

Product

Resources

About