Cumulative Residual Tsallis Entropy-Based Test of Uniformity and Some New Findings

Mohamed, Mohamed Said; Barakat, H. M.; Alyami, Salem A.; Elgawad, M. A. Abd

doi:10.3390/math10050771

Cited by 8 publications

(5 citation statements)

References 40 publications

(51 reference statements)

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“…Detailed mathematical formulations of entropy, computation of maximum values and mean values reported in [21][22][23] have been used in this paper to compute CFI1 to CFI7.…”

Section: Classification Of Pqementioning

confidence: 99%

Power Quality Detection and Categorization Algorithm Actuated by Multiple Signal Processing Techniques and Rule-Based Decision Tree

Singh¹,

Sharma²,

Garg³

et al. 2023

Sustainability

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This paper introduces a power quality (PQ) detection and categorization algorithm actuated by multiple signal processing techniques and rule-based decision tree (RBDT). This is aimed to recognize PQ events of simple nature and higher order multiplicity with less computational time using hybridization of the signal processing techniques. A voltage waveform with a PQ event (PQE) is processed using the Stockwell transform (ST) to compute the Stockwell PQ detection index (SPDI). The voltage waveform is also processed using the Hilbert transform (HT) to compute the Hilbert PQ detection index (HPDI). A voltage waveform is also decomposed using the Discrete Wavelet transform (DWT) to compute the classification feature index (CFI) [CFI1 to CFI4]. A combined PQ detection index (CPDI) is computed by multiplication of the SPDI, the HPDI and CFI1 to CFI4. Incidence of a PQE on a voltage signal is located with the help of a location PQ disturbance index (LPDI) which is computed by differentiating the CPDI with respect to time. CFI5, CFI6 and CFI7 are computed from the SPDI, the HPDI and the CPDI, respectively. Categorization of PQ events is performed using CFI1 to CFI7 by the rule-based decision tree (RBDT) with the help of simple decision rules. We conclude that the proposed algorithm is effective to identify the PQE with an accuracy of 98.58% in a noise-free environment and 97.62% in the presence of 20 dB SNR (signal-to-noise ratio) noise. Ten simple nature PQEs and eight combined PQ events (CPQEs) with multiplicity of two, three and four are effectively detected and categorized using the algorithm. The algorithm is also tested to detect a sag PQ event due to a line-to-ground (LG) fault incident on a practical distribution utility network. The performance of the investigated method is compared with a DWT-based technique in terms of accuracy of classification with and without noise, maximum computational time of PQ detection and multiplicity of PQE which can be effectively detected. A simulation is performed using the MATLAB software. MATLAB codes are used for modelling the PQE disturbances and the proposed algorithm using mathematical formulations.

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“…Detailed mathematical formulations of entropy, computation of maximum values and mean values reported in [21][22][23] have been used in this paper to compute CFI1 to CFI7.…”

Section: Classification Of Pqementioning

confidence: 99%

Power Quality Detection and Categorization Algorithm Actuated by Multiple Signal Processing Techniques and Rule-Based Decision Tree

Singh¹,

Sharma²,

Garg³

et al. 2023

Sustainability

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“…Entropy shares inherent similarities with uniformity, and entropy-based uniformity indices can effectively describe overall uniformity. Entropy has been used as a reference standard for assessing uniformity in some studies [ 21 , 22 ]. However, due to their relatively high computational complexity, these methods are rarely used for high-throughput field crop phenotype analysis.…”

Section: Introductionmentioning

confidence: 99%

UAV-Assisted Dynamic Monitoring of Wheat Uniformity toward Yield and Biomass Estimation

Yang,

Li,

et al. 2024

Plant Phenomics

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Crop uniformity is a comprehensive indicator used to describe crop growth and is important for assessing crop yield and biomass potential. However, there is still a lack of continuous monitoring of uniformity throughout the growing season to explain their effects on yield and biomass. Therefore, this paper proposed a wheat uniformity quantification method based on unmanned aerial vehicle imaging technology to monitor and analyze the dynamic changes in wheat uniformity. The leaf area index (LAI), soil plant analysis development (SPAD), and fractional vegetation cover were estimated from hyperspectral images, while plant height was estimated by a point cloud model from RGB images. Based on these 4 agronomic parameters, a total of 20 uniformity indices covering multiple growing stages were calculated. The changing trends in the uniformity indices were consistent with the results of visual interpretation. The uniformity indices strongly correlated with yield and biomass were selected to construct multiple linear regression models for estimating yield and biomass. The results showed that Pielou’s index of LAI had the strongest correlation with yield and biomass, with correlation coefficients of −0.760 and −0.801, respectively. The accuracies of the yield (coefficient of determination [ R 2 ] = 0.616, root mean square error [RMSE] = 1.189 Mg/ha) and biomass estimation model ( R 2 = 0.798, RMSE = 1.952 Mg/ha) using uniformity indices were better than those of the models using the mean values of the 4 agronomic parameters. Therefore, the proposed uniformity monitoring method can be used to effectively evaluate the temporal and spatial variations in wheat uniformity and can provide new insights into the prediction of yield and biomass.

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“…Accordingly, the residual Tsallis entropy is defined as

where

is the probability density function (PDF) of

is the survival function of X and

is the quantile function of

. Various properties, generalizations and applications of

are investigated by Asadi et al [ 4 ], Nanda and Paul [ 5 ], Zhang [ 6 ], Irshad et al [ 7 ], Rajesh and Sunoj [ 8 ], Toomaj and Agh Atabay [ 9 ], Mohamed et al [ 10 ], among others.…”

Section: Introductionmentioning

confidence: 99%

Tsallis Entropy of a Used Reliability System at the System Level

Kayid

Alshehri

2023

Entropy

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Measuring the uncertainty of the lifetime of technical systems has become increasingly important in recent years. This criterion is useful to measure the predictability of a system over its lifetime. In this paper, we assume a coherent system consisting of n components and having a property where at time t, all components of the system are alive. We then apply the system signature to determine and use the Tsallis entropy of the remaining lifetime of a coherent system. It is a useful criterion for measuring the predictability of the lifetime of a system. Various results, such as bounds and order properties for the said entropy, are investigated. The results of this work can be used to compare the predictability of the remaining lifetime between two coherent systems with known signatures.

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Cumulative Residual Tsallis Entropy-Based Test of Uniformity and Some New Findings

Cited by 8 publications

References 40 publications

Power Quality Detection and Categorization Algorithm Actuated by Multiple Signal Processing Techniques and Rule-Based Decision Tree

Power Quality Detection and Categorization Algorithm Actuated by Multiple Signal Processing Techniques and Rule-Based Decision Tree

UAV-Assisted Dynamic Monitoring of Wheat Uniformity toward Yield and Biomass Estimation

Tsallis Entropy of a Used Reliability System at the System Level

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