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Statements

Subject Item
dbr:Hilbert–Huang_transform
rdfs:label
Преобразование Гильберта — Хуанга 希爾伯特-黃轉換 Hilbert–Huang transform Transformada de Hilbert-Huang
rdfs:comment
Преобразование Гильберта — Хуанга (англ. HHT) — это преобразование, которое представляет собой разложение сигнала на эмпирические моды, с последующим применением к полученным компонентам разложения преобразования Гильберта. Преобразование Гильберта — Хуанга аналогично преобразованию Фурье, используемому в гармоническом анализе. The Hilbert–Huang transform (HHT) is a way to decompose a signal into so-called intrinsic mode functions (IMF) along with a trend, and obtain instantaneous frequency data. It is designed to work well for data that is nonstationary and nonlinear. In contrast to other common transforms like the Fourier transform, the HHT is an algorithm that can be applied to a data set, rather than a theoretical tool. 希爾伯特-黃轉換（Hilbert-Huang Transform），由台灣中央研究院院士黃鍔（Norden E. Huang）等人提出，將欲分析数据分解為本質模態函數（intrinsic mode functions, IMF），這樣的分解流程稱為經驗模態分解（Empirical Mode Decomposition, EMD）的方法。然後將IMF作希爾伯特轉換（Hilbert Transform），正確地獲得資料的瞬時頻率。此方法處理對象乃針對非穩態與非線性訊號。與其他數學轉換運算（如傅立葉變換）不同，希爾伯特-黃轉換算是一種應用在數據資料上的演算法，而非理論工具。 A transformada de Hilbert-Huang é uma técnica de decomposição de sinais em tempo-frequência utilizada nas mais diversas áreas, como biomedicina, química, economia e meteorologia. Rompe com o pressuposto clássico da decomposição de Fourier do sinal, em funções de base ortogonais definidas à priori, (independentemente do sinal)que são funções sinusoidais com Amplitude e frequência fixas. Esta técnica foi desenvolvida num dos organismos da NASA por Northen E. Huang em 1998, e tem sido aplicada a sinais nos mais variados ramos da ciência, que vão desde a engenharia à medicina.
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dbo:abstract
A transformada de Hilbert-Huang é uma técnica de decomposição de sinais em tempo-frequência utilizada nas mais diversas áreas, como biomedicina, química, economia e meteorologia. Rompe com o pressuposto clássico da decomposição de Fourier do sinal, em funções de base ortogonais definidas à priori, (independentemente do sinal)que são funções sinusoidais com Amplitude e frequência fixas. Esta técnica tem especial importância quando os sinais não têm características de linearidade e de estacionaridade, casos em que o tratamento clássicfo via FFT (Fast Fourier Transform) irá resultar com igual eficiência. A THH é um processo que comporta duas fases (EMD - Empirical mode decomposition e HSA - Hilbert Spectral analysis).As funções de base obtidas derivam do próprio sinal (base à posteriori) e têm amplitude variável e frequência também variável (instantânea). Esta técnica foi desenvolvida num dos organismos da NASA por Northen E. Huang em 1998, e tem sido aplicada a sinais nos mais variados ramos da ciência, que vão desde a engenharia à medicina. Преобразование Гильберта — Хуанга (англ. HHT) — это преобразование, которое представляет собой разложение сигнала на эмпирические моды, с последующим применением к полученным компонентам разложения преобразования Гильберта. Преобразование Гильберта — Хуанга аналогично преобразованию Фурье, используемому в гармоническом анализе. Преобразование Гильберта — Хуанга предложено Норденом Хуангом в 1995 году в ходе его работ в NASA для изучения поверхностных волн тайфунов. В 1998 г. метод был обобщен на анализ произвольных временных рядов коллективом соавторов [1]. Первоначально назывался англ. «EMD-HSA» — метод эмпирической модовой декомпозиции (англ. EMD) нелинейных и нестационарных процессов с последующим гильбертовым спектральным анализом (англ. HSA). В последующие годы, по мере расширения применения EMD-HSA для других отраслей науки и техники, вместо термина EMD-HSA был принят более короткий термин преобразования HHT. 希爾伯特-黃轉換（Hilbert-Huang Transform），由台灣中央研究院院士黃鍔（Norden E. Huang）等人提出，將欲分析数据分解為本質模態函數（intrinsic mode functions, IMF），這樣的分解流程稱為經驗模態分解（Empirical Mode Decomposition, EMD）的方法。然後將IMF作希爾伯特轉換（Hilbert Transform），正確地獲得資料的瞬時頻率。此方法處理對象乃針對非穩態與非線性訊號。與其他數學轉換運算（如傅立葉變換）不同，希爾伯特-黃轉換算是一種應用在數據資料上的演算法，而非理論工具。 The Hilbert–Huang transform (HHT) is a way to decompose a signal into so-called intrinsic mode functions (IMF) along with a trend, and obtain instantaneous frequency data. It is designed to work well for data that is nonstationary and nonlinear. In contrast to other common transforms like the Fourier transform, the HHT is an algorithm that can be applied to a data set, rather than a theoretical tool. The Hilbert–Huang transform (HHT), a NASA designated name, was proposed by Norden E. Huang et al. (1996, 1998, 1999, 2003, 2012). It is the result of the empirical mode decomposition (EMD) and the Hilbert spectral analysis (HSA). The HHT uses the EMD method to decompose a signal into so-called intrinsic mode functions (IMF) with a trend, and applies the HSA method to the IMFs to obtain instantaneous frequency data. Since the signal is decomposed in time domain and the length of the IMFs is the same as the original signal, HHT preserves the characteristics of the varying frequency. This is an important advantage of HHT since real-world signal usually has multiple causes happening in different time intervals. The HHT provides a new method of analyzing nonstationary and nonlinear time series data.
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