Concerto in G Major for Flute, Violin, and Orchestra

Margrave, Wendell; Gluck, Christoph Willibald; Scherchen, Hermann

doi:10.2307/890049

Cited by 1 publication

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“…As a result, a denoising process is often needed to separate the noise and actual signal. There are many de-noising methods in literature, such as linear Fourier filtering, 13) discrete wavelet transform 14) etc. When useful information is closely defined in frequency, and can well separate from the noise frequency components, then similar results can be obtained by wavelet transform and conventional methods.…”

Section: Wavelet De-noising Of Original Datamentioning

confidence: 99%

Identification of the Optimal Control Center for Blast Furnace Thermal State Based on the Fuzzy C-means Clustering

Huang

Zeng

et al. 2011

ISIJ Int.

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It is required to maintain silicon content in hot metal ([Si]) at a stable level to ensure smooth operation of the blast furnace ironmaking process. However, current blast furnace control strategy always leads to frequent fluctuation of silicon content in hot metal. To stabilize blast furnace operation, this article attempts to identify the optimum control centre of silicon content through exploring the operational data of blast furnace ironmaking process. A quantitative analysis of the impact of thermal state on the smelting efficiency and intensity is presented by combining wavelet denoising and fuzzy c-means (FCM) clustering. Simulation results show that the commonly adopted mean value of historical data is not necessarily the optimum state of blast furnace operation. There exists some optimum state lower than the mean value, under which higher smelting efficiency and intensity can be achieved. It is also proved that the "low silica smelting practice" attempt in the steel industry is feasible and meaningful.KEY WORDS: fuzzy C-means clustering; wavelet denoising; stability; silicon content in hot metal; hourly hot metal output.

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Section: Wavelet De-noising Of Original Datamentioning

confidence: 99%