This paper presents a new approach to the on-line tracking of pulverized coal and biomass fuels through flame spectrum analysis.A flame detector containing four photodiodes is used to derive multiple signals covering ...This paper presents a new approach to the on-line tracking of pulverized coal and biomass fuels through flame spectrum analysis.A flame detector containing four photodiodes is used to derive multiple signals covering a wide spectrum of the flame from visible,near-infrared and mid-infrared spectral bands as well as a part of far-infrared band.Different features are extracted in time and frequency domains to identify the dynamic "fingerprints" of the flame.Fuzzy logic inference techniques are employed to combine typical features together and infer the type of fuel being burnt.Four types of pulverized coal and five types of biomass are burnt on a laboratory-scale combustion test rig.Results obtained demonstrate that this approach is capable of tracking the type of fuel under steady combustion conditions.展开更多
The amplitude of frequency spectrum can he integrated with prohabilisfic data association （PDA） to distinguish the target with clutter echoes, especially in low SNR underwater environment. A new target-tracking algo...The amplitude of frequency spectrum can he integrated with prohabilisfic data association （PDA） to distinguish the target with clutter echoes, especially in low SNR underwater environment. A new target-tracking algorithm is presented which adopts the amplitude of frequency spectrum to improve target tracking in clutter. The prohabilisfic density distribution of frequency spectrum amplitude is analyzed. By simulation, the results show that the algorithm is superior to PDA. This approach enhances stability for the association probability and increases the performance of target tracking.展开更多
基金Supported by the Key Program of the National Natural Science Foundation of China(60534030)
文摘This paper presents a new approach to the on-line tracking of pulverized coal and biomass fuels through flame spectrum analysis.A flame detector containing four photodiodes is used to derive multiple signals covering a wide spectrum of the flame from visible,near-infrared and mid-infrared spectral bands as well as a part of far-infrared band.Different features are extracted in time and frequency domains to identify the dynamic "fingerprints" of the flame.Fuzzy logic inference techniques are employed to combine typical features together and infer the type of fuel being burnt.Four types of pulverized coal and five types of biomass are burnt on a laboratory-scale combustion test rig.Results obtained demonstrate that this approach is capable of tracking the type of fuel under steady combustion conditions.
基金This project was supported by the Defense Pre-Research Project of the‘Tenth Five-Year-Plan’of China (40105010101)
文摘The amplitude of frequency spectrum can he integrated with prohabilisfic data association （PDA） to distinguish the target with clutter echoes, especially in low SNR underwater environment. A new target-tracking algorithm is presented which adopts the amplitude of frequency spectrum to improve target tracking in clutter. The prohabilisfic density distribution of frequency spectrum amplitude is analyzed. By simulation, the results show that the algorithm is superior to PDA. This approach enhances stability for the association probability and increases the performance of target tracking.
