Hierarchical Refined Composite Multi-Scale Fractal Dimension and Its Application in Feature Extraction of Ship-Radiated Noise

Li, Yuxing; Liang, Lili; Zhang, Shuai

doi:10.3390/rs15133406

Cited by 4 publications

(3 citation statements)

References 42 publications

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“…This feature would be a very effective if it took advantage of the periodicity and the distribution of spectral components. Also, the multi-scale feature extraction of more than one level gives higher discrimination than a single resolution analysis [18][19][20][21][22][23][24] (Fig. 3).…”

Section: Fig 2 Sub-bands At Level-1 By Curveletmentioning

confidence: 99%

Multidirectional Analysis of Curvelet Against Skin Cancer

Kolekar,

Razak

et al. 2024

Preprint

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An elevated chance of getting another melanoma is associated with a personal history of the disease. Individuals who have already had a melanoma have a 2–5% probability of getting another one later. Compared to individuals whose initial melanoma was superficial spreading melanoma, those whose first melanoma was lentigo maligns melanoma or nodular melanoma are at a greater peril of emerging a secondary dominant cancer. Melanoma risk is double in those with a special antiquity of squamous cell carcinoma. The likelihood of getting melanoma is doubled if you have a particular times past of basal cell carcinoma. In addition, melanoma risk is higher in persons with actinic keratosis than in those without the condition. An automated technique for classifying melanoma, or skin cancer, is proposed in this work. An image of gathered data is used as the input for the proposed system, and various image handling methods remain smeared to improve the picture's characteristics. The curvelet technique is used to separate benign from malignant skin cancer and to collect relevant data from these pictures so that the classifier may be trained and tested. The basic wrapper curvelet's coefficients are the characteristics that are utilized for classification. Curvelet works well with images that have cartoon edges and aligned textures. In a database of digital photos, the three-layer back-propagation neural network classifier with curvelet has 75.6% recognition accuracy.

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Section: Fig 2 Sub-bands At Level-1 By Curveletmentioning

confidence: 99%

Multidirectional Analysis of Curvelet Against Skin Cancer

Kolekar,

Razak

et al. 2024

Preprint

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“…Nevertheless, the above two multiscale FD only reflect the complexity of time series information in a single frequency band. To solve this problem, Li et al proposed hierarchical refinement composite multiscale fractal dimension by introducing hierarchical analysis techniques to multiscale processing box dimensions, and the multiband characterization of complexity information was realized [14]. Yet, the above FD only considers data from a single channel of the time series, which cannot characterize the complexity information of a multichannel time series.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Multiscale Higuchi Fractal Dimension and Its Application to Mechanical Signals

Li,

Zhang,

Liang

et al. 2024

Fractal Fract

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Fractal dimension, as a common nonlinear dynamics metric, is extensively applied in biomedicine, fault diagnosis, underwater acoustics, etc. However, traditional fractal dimension can only analyze the complexity of the time series given a single channel at a particular scale. To characterize the complexity of multichannel time series, multichannel information processing was introduced, and multivariate Higuchi fractal dimension (MvHFD) was proposed. To further analyze the complexity at multiple scales, multivariate multiscale Higuchi fractal dimension (MvmHFD) was proposed by introducing multiscale processing algorithms as a technology that not only improved the use of fractal dimension in the analysis of multichannel information, but also characterized the complexity of the time series at multiple scales in the studied time series data. The effectiveness and feasibility of MvHFD and MvmHFD were verified by simulated signal experiments and real signal experiments, in which the simulation experiments tested the stability, computational efficiency, and signal separation performance of MvHFD and MvmHFD, and the real signal experiments tested the effect of MvmHFD on the recognition of multi-channel mechanical signals. The experimental results show that compared to other indicators, A achieves a recognition rate of 100% for signals in three features, which is at least 17.2% higher than for other metrics.

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“…To address this flaw, a refined composite multiscale process was proposed by changing the altering sampling point of the sub-series [30]. Li et al [31] first applied the refined composite multiscale process to fractal dimensions in 2023, presenting the hierarchical refined composite multiscale fractal dimension, which represents exceptional performance in feature extraction of SRN. Nevertheless, the refined composite multiscale process still has the disadvantage of significantly shortening the length of coarse-grained series when the scale factor is large, which leads to a decrease in accuracy.…”

Section: Introductionmentioning

confidence: 99%

Variable-Step Multiscale Katz Fractal Dimension: A New Nonlinear Dynamic Metric for Ship-Radiated Noise Analysis

Li,

Zhou,

Jiao

2023

Fractal Fract

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The Katz fractal dimension (KFD) is an effective nonlinear dynamic metric that characterizes the complexity of time series by calculating the distance between two consecutive points and has seen widespread applications across numerous fields. However, KFD is limited to depicting the complexity of information from a single scale and ignores the information buried under different scales. To tackle this limitation, we proposed the variable-step multiscale KFD (VSMKFD) by introducing a variable-step multiscale process in KFD. The proposed VSMKFD overcomes the disadvantage that the traditional coarse-grained process will shorten the length of the time series by varying the step size to obtain more sub-series, thus fully reflecting the complexity of information. Three simulated experimental results show that the VSMKFD is the most sensitive to the frequency changes of a chirp signal and has the best classification effect on noise signals and chaotic signals. Moreover, the VSMKFD outperforms five other commonly used nonlinear dynamic metrics for ship-radiated noise classification from two different databases: the National Park Service and DeepShip.

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Hierarchical Refined Composite Multi-Scale Fractal Dimension and Its Application in Feature Extraction of Ship-Radiated Noise

Cited by 4 publications

References 42 publications

Multidirectional Analysis of Curvelet Against Skin Cancer

Multidirectional Analysis of Curvelet Against Skin Cancer

Multivariate Multiscale Higuchi Fractal Dimension and Its Application to Mechanical Signals

Variable-Step Multiscale Katz Fractal Dimension: A New Nonlinear Dynamic Metric for Ship-Radiated Noise Analysis

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