2,268 research outputs found
Experimental study on the spray characteristics in the spray drying absorber
No full textA pilot-scale spray drying absorber (SDA) system was set up to evaluate the effect of spray characteristics on flue gas desulfurization efficiency. Varying spray injection conditions and inlet gas conditions, SO2 concentrations were measured using a combustion gas analyzer. As a reagent, a slaked lime (Ca(OH)(2)) slurry was treated with flue gas containing SO2. The influence of each parameter is determined by the compromise between the droplet absorption capability and SO2 gas supply to the region of the droplet cloud. When the SO2 molecules are sufficiently supplied to the wet droplet surface, the variation of the total droplet surface area plays a dominant role in the desulfurization process. The conditions of low inlet gas temperature and low slurry feed rate are included in this case. However, when the droplets of sprayed slurries are too small or evaporation proceeds too fast, the SO2 molecules cannot be sufficiently transferred to the droplet surface. The excess calcium sorbents in the droplets dry out before they react with the hot gas. Consequently, the desulfurization efficiency as well as the utilization efficiency of Ca decreases for the smaller droplets. This trend appears to be dominant in conditions of high gas temperature and large slurry feed rate with the small Sauter mean diameter (SMD) of spray droplets. From the experimental results, we proposed two empirical models for the desulfurization efficiency and operating conditions
Permutation correction of filter bank ICA using static channel characteristics
No full textThis paper exploits static channel characteristics to provide a precise solution to the permutation problem in filter bank approach to Independent Component Analysis (ICA). The filter bank approach combines the high accuracy of time domain ICA and the computational efficiency of frequency domain ICA. Decimation in each sub-band helps in better formulation of the directivity patterns. The nulls of the directivity patterns are dependent on the location of the source signals and this property is used for resolving the permutation problem. The experimental results, show a good behavior with reduced computational complexity and do not require non-stationarity of the signals.This research was supported as a Brain Neuroinformatics Research Program by
Korean Ministry of Science and Technology
Adaptive noise cancelling based on independent component analysis
No full textA method for adaptive noise cancelling based on independent component analysis (ICA) is presented. Although the conventional least-mean-squares (LMS) algorithm removes noise components based on second-order correlation, the proposed algorithm can utilise higher-order statistics. Experimental results show that the proposed algorithm provides considerable performance improvement.This work was supported by the Brain Science & Engineering Research Program sponsored by Korean Ministry of Science and Technology
Directionally constrained filterbank ICA
No full textA modification is proposed to the independent component analysis (ICA)-based filterbank approach in consideration to its structural similarity with binaural auditory model of sound source localization. The estimated sound locations provide an additional cue to the learning algorithm, which is utilized for initialization and imposition of directional constraints on the subband separation networks. Directionally constrained filterbank ICA (DC-FBICA) gives faster convergence and improves separation performance for noisy mixtures having significant spectral overlap among the convolved mixture and the corrupting noise. However, only slight improvement in separation performance is observed when the additive noise is a low frequency noise, although faster convergence is still observed
Top-down attention to complement independent component analysis for blind signal separation
No full textFor robust speech recognition in real-world noisy environments, we present an algorithm to incorporate blind signal separation based on independent component analysis (ICA) and top-down attention processing. While ICA-based unmixing networks learn the inverse of mixing characteristics in frequency domain, their performance is limited by mismatches between the real-world mixing characteristics and assumptions of the ICA algorithm. The top-down process from a multiplayer Perceptron (MLP) classifier provides additional information on the speech signal, and fine-tunes the networks to compensate for the mismatches. For noisy speech signals recorded in a real office environment, the developed algorithm demonstrated great improvements on recognition performance. (C) 2002 Published by Elsevier Science B.V.This research was supported by the Brain Science and Engineering Research Program
from Korean Ministry of Science and Technology
A filter bank approach to independent component analysis and its application to adaptive noise cancelling
No full textWe present a filter bank (FB) approach to perform independent component analysis for adaptive noise cancelling. This approach is based on FBs, and its decimation provides much less computational complexity and faster convergence speed than the time-domain approach. In addition, the approach does not have a performance limitation unlike the frequency-domain approach. One can select the number of filters in the FB regardless of reverberation and implement the method to fit for parallel processing. We verify the effectiveness of the FB approach through simulations on adaptive noise cancelling. (C) 2003 Elsevier B.V. All rights reserved.This work was supported as the Brain Neuroinformatics Research Program sponsored
by Korean Ministry of Science and Technology
Blind equalization using direct channel estimation
No full textIn performing blind equalization, we propose a direct channel estimation method based on entropy-maximization of input signal with its known probability density function. That is, the proposed method estimates filter coefficients of the channel instead of equalizing filter coefficients which most of equalization methods try to estimate. Because the channel usually has a much shorter length than the equalizing filter, this method requires much smaller parameters to be estimated, and the channel can be equalized with much less computational demands. In addition, simulation results show that the proposed method can recover signals with a much smaller error than conventional methods.This work was supported by the Brain Neuroinformatics Research Program sponsored
by Korean Ministry of Science and Technology
- …
