• Clean Technology
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• v.15 no.3
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• pp.194-201
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• 2009

Reductive reactivity of zero-valent iron nanoparticles was investigated for removal of nitrate-nitrogen which is considered one of the major water pollutants. To elucidate the difference in reactivity between preparation methods, iron nanoparticles were synthesized respectively from microemulsion and aqueous solution of ferric ions. Iron nanoparticles prepared from microemulsion were deposited on aluminum by electrophoretic method, and their reaction kinetics was compared to that of the same nanoparticles suspended in aqueous batch reaction. With an approximation of pseudo-first-order reaction, rate constants for suspended nanoparticles prepared from microemulsion and dilute aqueous solution were $3.49{\times}10^{-2}min^{-1}$ and $1.40{\times}10^{-2}min^{-1}$, respectively. Iron nanoparticles supported on aluminum showed ca. 30% less reaction rate in comparison with the identical nanoparticles in suspended state. However, supported nanoparticles showed the superior effectiveness in terms of nitrate-nitrogen removal per zero-valent iron input especially when excess amounts of nitrates were present. Iron nanoparticles deposited on aluminum maintained reductive reactivity for more than 3 hours, and produced nitrogen gas as a final reduction product of nitrate-nitrogen.

• The Journal of the Acoustical Society of Korea
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• v.16 no.5
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• pp.112-121
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• 1997

In this paper, we study a model parameter compensation method for noise-robust speech recognition. We study model parameter compensation on a sentence by sentence and no other informations are used. Parallel model combination(PMC), well known as a model parameter compensation algorithm, is implemented and used for a reference of performance comparision. We also propose a modified PMC method which tunes model parameter with an association factor that controls average variability of gaussian mixtures and variability of single gaussian mixture per state for more robust modeling. We obtain a re-estimation solution of environmental variables based on the expectation-maximization(EM) algorithm in the cepstral domain. To evaluate the performance of the model compensation methods, we perform experiments on speaker-independent isolated word recognition. Noise sources used are white gaussian and driving car noise. To get corrupted speech we added noise to clean speech at various signal-to-noise ratio(SNR). We use noise mean and variance modeled by 3 frame noise data. Experimental result of the VTS approach is superior to other methods. The scheme of the zero order VTS approach is similar to the modified PMC method in adapting mean vector only. But, the recognition rate of the Zero order VTS approach is higher than PMC and modified PMC method based on log-normal approximation.