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- 저자 : Setayeshi, S. (검색결과 4 건)
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A Harmonic-Based Biologically Inspired Approach to Monaural Speech Separation
Rabiee, A.,Setayeshi, S.,Soo-Young Lee IEEE 2012 IEEE signal processing letters Vol.19 No.9
<P>This letter proposes a computational auditory scene analysis (CASA) model for monaural speech separation. In this model, we integrate three biologically inspired approaches for: auditory spectrogram generation, analysis of its spectro-temporal content, and tracking its harmonic structure. In a top-down process, the estimated ideal binary mask (EIBM) is calculated using the spectral amplitude of the extracted spectrograms to enhance the harmonic filters for separation. Experimental results showed that our model outperformed the harmonic magnitude suppression technique in both signal-to-interference ratio and percentage of crosstalk. Moreover, the result is comparable with a current state-of-the-art system.</P>
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Saeed Heidary,Saeed Setayeshi,Mohammad Ghannadi-Maragheh 한국물리학회 2014 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.65 No.5
The aim of this study is to compare the adaptive neuro-fuzzy inference system (ANFIS) andthe artificial neural network (ANN) to estimate the cross-talk contamination of 99mTc / 201Tlimage acquisition in the 201Tl energy window (77 ± 15% keV). GATE (Geant4 Application inEmission and Tomography) is employed due to its ability to simulate multiple radioactive sourcesconcurrently. Two kinds of phantoms, including two digital and one physical phantom, are used. Inthe real and the simulation studies, data acquisition is carried out using eight energy windows. TheANN and the ANFIS are prepared in MATLAB, and the GATE results are used as a training dataset. Three indications are evaluated and compared. The ANFIS method yields better outcomes fortwo indications (Spearman’s rank correlation coefficient and contrast) and the two phantom resultsin each category. The maximum image biasing, which is the third indication, is found to be 6%more than that for the ANN.
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Nazemi, E.,Feghhi, S.A.H.,Roshani, G.H.,Gholipour Peyvandi, R.,Setayeshi, S. Korean Nuclear Society 2016 Nuclear Engineering and Technology Vol.48 No.1
Void fraction is an important parameter in the oil industry. This quantity is necessary for volume rate measurement in multiphase flows. In this study, the void fraction percentage was estimated precisely, independent of the flow regime in gas-liquid two-phase flows by using ${\gamma}-ray$ attenuation and a multilayer perceptron neural network. In all previous studies that implemented a multibeam ${\gamma}-ray$ attenuation technique to determine void fraction independent of the flow regime in two-phase flows, three or more detectors were used while in this study just two NaI detectors were used. Using fewer detectors is of advantage in industrial nuclear gauges because of reduced expense and improved simplicity. In this work, an artificial neural network is also implemented to predict the void fraction percentage independent of the flow regime. To do this, a multilayer perceptron neural network is used for developing the artificial neural network model in MATLAB. The required data for training and testing the network in three different regimes (annular, stratified, and bubbly) were obtained using an experimental setup. Using the technique developed in this work, void fraction percentages were predicted with mean relative error of <1.4%.
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E. Nazemi,S.A.H. FEGHHI,G.H. Roshani,R. Gholipour Peyvandi,S. Setayeshi 한국원자력학회 2016 Nuclear Engineering and Technology Vol.48 No.1
Void fraction is an important parameter in the oil industry. This quantity is necessary forvolume rate measurement in multiphase flows. In this study, the void fraction percentagewas estimated precisely, independent of the flow regime in gaseliquid two-phase flows byusing g-ray attenuation and a multilayer perceptron neural network. In all previous studiesthat implemented a multibeam g-ray attenuation technique to determine void fractionindependent of the flow regime in two-phase flows, three or more detectors were usedwhile in this study just two NaI detectors were used. Using fewer detectors is of advantagein industrial nuclear gauges because of reduced expense and improved simplicity. In thiswork, an artificial neural network is also implemented to predict the void fraction percentageindependent of the flow regime. To do this, a multilayer perceptron neuralnetwork is used for developing the artificial neural network model in MATLAB. Therequired data for training and testing the network in three different regimes (annular,stratified, and bubbly) were obtained using an experimental setup. Using the techniquedeveloped in this work, void fraction percentages were predicted with mean relative errorof <1.4%.
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