Multiscale Higuchi’s fractal dimension method

Yilmaz, Adil; Ünal, Gazanfer

doi:10.1007/s11071-020-05826-w

Cited by 13 publications

(7 citation statements)

References 43 publications

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“…Dimensi fraktal Higuchi merupakan metode komputasi non-linear cepat yang hasilnya lebih akurat dibandingkan dengan metode lainnya (Yilmaz & Unal, 2020)…”

Section: E Higuchiunclassified

Penerapan Dimensi Fraktal Higuchi Dan K-Nearest Neighbor Dalam Klasifikasi Passeriformes (Burung Pengicau) Berdasarkan Suara

Yania,

Juniati

2023

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Passeriformes adalah salah satu kelas aves yang merupakan jenis burung pengicau. Tingkat konservasi dari spesies Passeriformes masih relatif rendah dan populasinya sudah mulai menurun, sehingga kelestarian Passeriformes perlu dipertahankan dari kepunahan maupun penurunan populasinya. Burung memiliki habitat yang beraneka ragam, sehingga menyebabkan keterbatasan dalam mengetahui ada atau tidaknya spesies burung di suatu wilayah. Suara kicauan dapat digunakan untuk mengenali jenis burung berdasarkan suaranya. Dengan dimensi fraktal bisa melakukan analisis suara Passeriformes berdasarkan ciri sinyal suaranya. Penelitian ini akan mengklasifikasikan Passeriformes berdasarkan suara menggunakan metode Higuchi serta K-Nearest Neighbor. Langkah awal yaitu pra-pemrosesan, selanjutnya adalah proses ekstraksi ciri menggunakan Discrete Wavelet Transform (DWT) dan dekomposisi wavelet 5 level menggunakan tipe mother wavelet Daubechies4. Selanjutnya yaitu menghitung nilai dari dimensi fraktal dengan memakai metode Higuchi. Setelah memperoleh nilai dimensi fraktal, data dibagi menjadi dua yaitu data pelatihan dan data pengujian. Selanjutnya data diklasifikasikan menggunakan algoritma K- Nearest Neighbor. Dalam penelitian ini, telah diperoleh nilai dengan akurasi tertinggi sebesar 90% dimana perbandingan data pelatihan maupun data pengujian adalah 9:1, dengan nilai Kmax =60, serta nilai k pada K-NN adalah 1, 2 dan 5. Sehingga, dapat disimpulkan penerapan dimensi fraktal Higuchi serta K-Nearest Neighbor dapat digunakan dalam mengklasifikasikan Passeriformes berdasarkan suara. Kata Kunci: Passeriformes, Dimensi Fraktal Higuchi, K-Nearest Neighbor (K-NN).

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“…Dimensi fraktal Higuchi merupakan metode komputasi non-linear cepat yang hasilnya lebih akurat dibandingkan dengan metode lainnya (Yilmaz & Unal, 2020)…”

Section: E Higuchiunclassified

Penerapan Dimensi Fraktal Higuchi Dan K-Nearest Neighbor Dalam Klasifikasi Passeriformes (Burung Pengicau) Berdasarkan Suara

Yania,

Juniati

2023

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“…Fractal has been widely studied [43] and fractal dimension is also attract a lot of focues [44,45]. For example, the fractal dimension of Sierpinski triangle is ln3 ln2 [46], the fractal dimension of Koch curve is ln4 ln3 [47], and the fractal dimension of Cantor sets is ln2 ln3 [48].…”

Section: Fractal and Information Dimensionmentioning

confidence: 99%

Information fractal dimension of mass function

Qiang,

Deng,

Cheong

2021

Preprint

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Fractal plays an important role in nonlinear science. The most important parameter to model fractal is fractal dimension. Existing information dimension can calculate the dimension of probability distribution. However, given a mass function which is the generalization of probability distribution, how to determine its fractal dimension is still an open problem of immense interest. The main contribution of this work is to propose an information fractal dimension of mass function. Numerical examples are illustrated to show the effectiveness of our proposed dimension. We discover an important property in that the dimension of mass function with the maximum Deng entropy is ln3 ln2 ≈ 1.585, which is the well-known fractal dimension of Sierpiński triangle.

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“…In 2018, Chen et al combined multiscale processing techniques with the information dimension and proposed the multiscale information dimension, which comprehensively reflects the non-linear and non-smooth properties of the original time series [12]. Similar to the idea of multiscale information dimension, Yilmaz et al proposed the multiscale Higuchi FD (HFD), inspired by multiscale sample entropy, which accurately identifies specific classes of random and chaotic time series [13]. Nevertheless, the above two multiscale FD only reflect the complexity of time series information in a single frequency band.…”

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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Multiscale Higuchi’s fractal dimension method

Cited by 13 publications

References 43 publications

Penerapan Dimensi Fraktal Higuchi Dan K-Nearest Neighbor Dalam Klasifikasi Passeriformes (Burung Pengicau) Berdasarkan Suara

Penerapan Dimensi Fraktal Higuchi Dan K-Nearest Neighbor Dalam Klasifikasi Passeriformes (Burung Pengicau) Berdasarkan Suara

Information fractal dimension of mass function

Multivariate Multiscale Higuchi Fractal Dimension and Its Application to Mechanical Signals

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